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Avionics system Architectures for Software-Defined Spacecraft 1

11th EAI International Conference on MOBILe Wireless MiddleWARE, Operating systems and Applications, MOBILWARE 2022

作者： He, Xiongwen Li, Jionghui Bai, Fan Jia, Xiaoyu Huang, Xiaofeng Xu, Mingwei Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

Developing upon open system architecture, Software-Defined Spacecraft, as a new generation of spacecraft, can support payload plug-and-play, application software loading as needed, and system function reconfiguration on demand. Therefore, the Software-Defined Spacecraft is able to solve the design and application limitations of traditional specific-designed spacecraft, making it become a significant developing trend of future spacecraft. Regarding to the software defined spacecraft bus, this paper analyzes the requirements for avionics system on the aspects of space field development, technology development and corresponding applications. Then, combining with the development status, the avionics system architecture of software defined spacecraft is proposed, along with the related key technologies. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Application programs

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Experimented Multi-Objective Teaching-Learning-Based Optimization for Medical Data Analysis

Experimented Multi-Objective Teaching-Learning-Based Optimiz...

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7th International Conference on Data Science and Its Applications, ICoDSA 2024

作者： Darmawahyuni, Annisa Nurmaini, Siti Tutuko, Bambang Naufal Rachmatullah, Muhammad Marcelino, Jordan Isdwanta, Rendy Firdaus, Firdaus Iriani Sapitri, Ade Islami, Anggun Faculty of Engineering Universitas Sriwijaya Indonesia Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya South Sumatera Palembang Indonesia

ISBN: (纸本)9798350365351

Feature selection (FS) analyzes the most important features to enhance classification accuracy during preprocessing. The objective of FS techniques is to reduce the number of input variables and computational load to achieve optimal classification performance. However, finding the best features is challenging due to high dimensionality, with many features but limited data samples. Metaheuristic optimization algorithms (MOAs) are crucial in creating the best subset of features through exploration and exploitation phases. This study experiments with Teaching-Learning-based Optimization (TLO) as MOA from several diverse binary medical datasets. TLO draws attention to its excellence in improving performance with minimal features. TLO is a fairly straightforward algorithm without algorithm-specific parameters, with rapid convergence and implementation yet effectiveness. In this study, we have also conducted four classifiers to obtain the best model for minimizing the features and maximizing the performance of the model, i.e., decision tree (DT), random forest (RF), k-Nearest Neighbour (KNN), and support vector machine (SVM) with the default parameter of each classifier. As the result, TLO shows its excellence in achieving minimal features, but still outstanding performance with 100% accuracy, sensitivity, specificity, and precision. TLO is successfully applied to varying diverse medical datasets with varying number of features. © 2024 IEEE.

关键词： Contrastive Learning

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Massive Fabrication of Carbon Nanotube Transistors by Surface Tension-Driven Inkjet-Printing Method 5

Massive Fabrication of Carbon Nanotube Transistors by Surfac...

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5th IEEE International Conference on Flexible and Printable Sensors and systems, FLEPS 2023

作者： Park, Soohyun Shin, Minhye Kang, Hongki Lee, Yoonhee Division of Electronics and Information System Daegu42988 Korea Republic of Department of Electrical Engineering and Computer Science Daegu42988 Korea Republic of

ISBN: (纸本)9781665457330

Carbon nanotube field-effect transistors (CNTFETs) have been ideal nanoelectronics in semiconductor technologies with exceptional electrical properties. Scaling the CNT -FET fabrication enables the expansion of the application fields. However, placing nanotubes with uniform alignment and controlled density to the electrode contacts has been the maj or challenge in massive manufacturing. Here, we introduce the in-place inkjet-printing method for fabricating CNTFETs with controlled numbers of connected CNTs at a single CNT level. A picoliter (pL) drop of CNT ink is printed to the pre-patterned electrode arrays in parallel over a four-inch silicon wafer, allowing adaptive manufacturing without additional lithographic processes. Drops form thin films on the metal electrode patterns driven by the surface tension flow, diluting the surface density of CNT on the one-micrometer channel gap and avoiding bundled coffee ring edges. Finally, single CNTs are correctly bridged to the electrodes with high statistical yield. This process enables reproducible and high-throughput single CNT deposition, integrating into the FET manufacturing process, and can be used for versatile nanoelectronics applications, such as biosensors. © 2023 IEEE.

关键词： Carbon nanotubes

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Sentiment Analysis of Public Opinion on Twitter about the Implementation of the Merdeka Curriculum Using the Support Vector Machine Algorithm

Sentiment Analysis of Public Opinion on Twitter about the Im...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Ramadhanti, Intan Widodo Nugraheni, Murien Universitas Negeri Jakarta Informatics and Computer Engineering Education Jakarta Indonesia Universitas Negeri Jakarta Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798350369359

In 2022 the Head of Standards, Curriculum, Education Assessment (BSKAP) No. 0441HIKR/2022 issued a Decree (SK) regarding Education Units for the Implementation of the Merdeka Curriculum in 140 thousand educational units in Indonesia. The implementation of this new curriculum raises various public opinions on social media Twitter. Therefore, research on the analysis of public sentiment on the implementation of the Merdeka Curriculum was carried out. The tweets taken are tweets with positive and negative sentiments. This study aims to see whether the SVM algorithm is good at carrying out text classification for sentiment analysis of the Merdeka Curriculum by looking at the accuracy value and describing how the public's sentiment on Twitter is about the Merdeka Curriculum. The data used is a dataset of 1,186 tweets, that is 363 positive tweets and 823 negative tweets. The results showed that the accuracy of the Support Vector Machine algorithm with linear, polynomial, and sigmoid kernels was 91.82% and the RBF kernel was 89.88%. As well as seen from the results of the sentiment analysis of implementing the Merdeka Curriculum there were more negative responses, one of which was regarding too many assignments and projects that made students feel tired and stressed. © 2023 IEEE.

关键词： Support vector machines

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Chatbots: Understanding Their World, Classification, and Their Current State 2nd

Chatbots: Understanding Their World, Classification, and The...

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Emergent Converging Technologies and Biomedical systems Select Proceedings of the 2nd International Conference, ETBS 2022

作者： Singh, Amit Sharma, K.K. Kumar, Parveen Vats, Sandeep Kumar Computer Science and Engineering Department Sanskriti University Mathura UP Chatta India Centre for Railways Information System Chanakyapuri Delhi India

ISBN: (纸本)9789819922703

An extraordinary growth is being witnessed for mobile devices and their ever-growing computational powers. Also, the chip demand is very high. This demand is not only coming from the software industry, but almost all the other sectors also contribute to this enormous growth. With the recent innovations in chip manufacturing, complemented with very high demands, compute power costs are getting reduced at a significant rate. The computation power of chips fuels the artificial intelligence (AI) technologies to harness them. AI is a very computation resource demanding technology, and, in many cases, it requires specialized computational resources. Due to deep mobile penetration in the society, AI technologies such as Data Science and NLP are taking great pace. On the other hand, chatbots are gaining momentum beyond customer care and typical use cases. This paper analyzes the history, growth, and new trends of chatbots. Especially the impact of AI on bot technologies and the road ahead. This paper will take a look at the chatbot journey. It also examines where chatbots are making an impact on human lives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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Moodle-based Learning Management system Design as a Learning Media at SMKN 40 Jakarta 7

Moodle-based Learning Management System Design as a Learning...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Setya, Oktaviano Nanda Prima Widodo Nugraheni, Murien State Jakarta University Informatics and Computer Engineering Education Jakarta Indonesia State Jakarta University Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798350307504

Indonesia is currently in the final stages of the COVID-19 pandemic, which has changed the world of the online learning. Online learning has many benefits, one of which students can learn anywhere and anytime. SMK Negeri 40 Jakarta is one of the vocational high schools located in DKI Jakarta. Based on observation at SMKN 40 Jakarta, teachers use a learning management system for daily learning. Therefore, a Moodle-based learning management system is needed. Learning Management system is a web application to manage various learning activities automatically, which is built using Moodle platform with RAD (Rapid Application Development) methodology. The result is a Moodle-based learning management system application that can be useful in supporting the teaching and learning process at school. Based on the results of black box testing and user acceptance testing, the learning management system created is very good, therefore it can be concluded this application can be accepted and implemented at SMK Negeri 40 Jakarta. © 2023 IEEE.

关键词： Black-box testing

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment

Application of Artificial Intelligence Methods for Generatin...

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Machine Learning and Natural Language Processing (MLNLP), International Conference on

作者： Nazipov Rustam Salavatovich Avksentieva Elena Yurievna Faculty of Software Engineering and Computer System ITMO University Saint Petersburg Russia Faculty of Software Engineering and Computer System ITMO University Saint-Petersburg Russia

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

The present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. The research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. The methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. The study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context.

关键词： Analytical models Statistical analysis Reviews Neural networks Process control Quality control Machine learning Chatbots Mathematical models Context modeling

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Resilience assessment framework toward interdependent bus-rail transit network:Structure,critical components,and coupling mechanism

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Communications in Transportation Research 2023年第1期3卷 118-129页

作者： Bing Liu Xiaoyue Liu Yang Yang Xi Chen Xiaolei Ma School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Civil&Environmental Engineering University of UtahSalt Lake CityUT84112USA School of Computer Science and Engineering Beihang UniversityBeijing100191China Key Laboratory of Intelligent Transportation Technology and System Ministry of EducationBeijing100191China

Understanding the interdependent nature of multimodal public transit networks(PTNs)is vital for ensuring the resilience and robustness of transportation ***,previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs,neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal ***,this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism,structural and functional characteristics of bus-rail transit networks(BRTNs).In this framework,a network performance metric is proposed by considering the passengers’travel behaviors under various ***,stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or *** proposed framework is validated via a case study of a BRTN in Beijing,*** indicate that the rail transit network(RTN)plays a crucial role in maintaining network function and resisting external disturbances in the interdependent ***,the coupling interactions between the RTN and bus transit network(BTN)exhibit distinct characteristics under infrastructure component disruption and functional *** findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

关键词： Complex network Bus-rail transit network Resilience analysis Interdependent coupling relationship Classification analysis

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Multi - Task Learning with Convolutional Neural Network for Packet Format Detection and Modulation Classification of Wireless LAN 13

Multi - Task Learning with Convolutional Neural Network for ...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Putra, Dody Ichwana Pratama, M. Harry Bintang Nagao, Yuhei Kurosaki, Masayuki Ochi, Hiroshi Graduate School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we propose multi-task learning (MTL) using a convolutional neural network (CNN) to simultaneously perform packet format detection and modulation classification of wireless LAN (WLAN) signals. Compared to single-task learning, the main advantage of this method is the high classification accuracy and rapid learning with a lightweight neural network model. These are achieved because of learning shared knowledge (i.e., model weight or gradients) between related tasks. This shared representation model increases data efficiency and reduces overfitting. The WLAN signals generated by the Matlab wave generator are used to validate and test the model. The results show that even with different time offsets, the proposed method has an accuracy of 98% for packet format detection and 86% for modulation classification. © 2022 IEEE.

关键词： Orthogonal frequency division multiplexing

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Towards High-throughput and Low-latency Billion-scale Vector Search via CPU/GPU Collaborative Filtering and Re-ranking 23

Towards High-throughput and Low-latency Billion-scale Vector...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present FusionANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of FusionANNS lies in CPU/GPU collaborative filtering and re-ranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. © 2025 FAST. All Rights Reserved.

关键词： Collaborative filtering

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