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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Semantic Knowledge-Based Mission Planning Method According to Robot Characteristics in Outdoor Environment 1

引用

18th International Conference on Intelligent Autonomous systems (IAS)

作者： Bae, Sanghyeon Choi, Junhyeon Kuc, Taeyong Park, Hyunjin Lee, Kwanghee Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Korea Inst Ind Technol Robot R&D Grp Ansan 15588 South Korea

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

In the field of robots, many studies have been proposed to introduce advanced cognitive science into robot systems based on existing numerical information. These research directions allow robots to utilize their knowledge to simplify, understand, and efficiently plan their tasks. In this paper, we propose a semantic knowledge-based robot mission planning method in an irregular three-dimensional environment. We introduce an advanced robot mission planning method that defines the knowledge of robots and driving places based on ontology and utilizes the defined semantic knowledge. To this end, we define a robot domain based on Planning Domain Definition Language (PDDL) and solve it through a classical planner to plan the mission. Finally, we validate the suitability of the proposed method by conducting experiments with real-world robots within the campus environment and comparing them with general PDDL-based methods.

关键词： Robot mission planning Planning domain definition language Semantic knowledge

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Pragmatic Statistics: Likert-Type Questionnaires Processing 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Ginters, Egils Riga Tech Univ LV-1658 Riga Latvia Sociotech Syst OU Sakala St 7-2 EE-10141 Tallinn Estonia

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

Today, almost all systems are sociotechnical. The validation of the technical component is based on quantitative measurements and comparisons with various standards. On the other hand, the assessment of social factors is not so simple and is based on measurements of the audience's attitude. Usually, Likert-type surveys are used for such purposes. In order to obtain adequate information about the attitude of the audience, first of all, the survey must be properly prepared and a suitable audience must be selected, but the obtained results must be statistically processed. There are dozens of different statistical methods, but it is important to determine the most useful set of methods for different situations. The article has been developed in the form of a short manual/handbook, including calculation examples and recommendations. The article will be useful for both students and researchers, as well as project managers who need to measure the attitude of the target audience.

关键词： Likert-Type Questionnaire Survey Processing Data Understanding Reliability Dichotomous Data Shapiro-Wilk Test Cronbach's Alpha Attitude Measurements Descriptive Statistics Statistical Processing Kolmogorov-Smirnov Test Fleiss Kappa Statistical Methods Meaningfulness Interrelated Influence Pearson Correlation Adjusted Wald Technique

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Active Risk Mitigation for Unmanned Aerial systems

Active Risk Mitigation for Unmanned Aerial Systems

引用

Intelligent systems Conference (IntelliSys)

作者： Kendall, Andrew Clarke, John-Paul Georgia Inst Technol Atlanta GA 30332 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9783031477201;9783031477218

Efforts are underway to introduce Unmanned Aerial systems (UAS) into routine cargo operations within the National Airspace System (NAS). Many argue that UAS operations will be riskier than operation with pilots onboard the aircraft because there are no humans onboard to respond to off-nominal events. In this paper, we present a safety assessment methodology where agent-based modeling and rare event estimation are used to determine the risks due to component and system failures, and thereby accident probabilities. The methodology is applied to the final approach and landing phases of flight within the context of an investigation of the performance required from decision-making agents when wind shear, navigation errors, and engine failures occur. Results indicate that the online safety assessment methodology is able to actively mitigate risk and provide the autonomous system safety performance required to comply with FAA airworthiness regulations.

关键词： Unmanned Aerial systems Autonomous System Safety Rare Event Estimation

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Dropout in Computer Science, systems Engineering and Software Engineering Programs 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Bayona-Ore, Sussy Univ Autonoma Peru Invest Panamer Sur Km 16-3 Lima Peru

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

Student dropout as a decision-making process is complex and conditioned by various factors. Despite the existence of regulatory frameworks and the efforts made by universities to implement programs to retain students, the objectives do not always achieve. The Computer Science, systems Engineering and Software Engineering careers are no strangers to this problem. This article presents the results of a bibliometric analysis to determine the aspects related to student dropout in these careers. The analysis of keywords co-occurrence shows that, out of a total of 282 keywords corresponding to 48 articles, 41 met the condition. Five clusters were formed relating to the prediction of student desertion, teaching systems, retention models, careers and desertion in higher education. The factors conditioning student desertion highlighted in the studies include communication among teachers, usefulness of the degree, cognitive gain, university entrance exam score, gender, place of residence, number of siblings, family income, English qualification, mathematics qualification and administration, among others. Understanding the factors involved in student dropout will allow these to be considered in the strategies for student dropout prevention and student retention.

关键词： Dropout Higher education Software engineering Computer science ICT

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Automated Spatiotemporal Modeling for Real-Time Data-Driven Actionable Insights

Automated Spatiotemporal Modeling for Real-Time Data-Driven ...

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Intelligent systems Conference (IntelliSys)

作者： Latapie, Hugo Gabriel, Mina Srinivasan, Sidarth Kompella, Ramana Thorisson, Kristinn R. Wang, Pei Cisco Syst Cisco Res San Jose CA 95134 USA Reykjavik Univ Dept Comp Sci Ctr Anal & Design Intelligent Agents Reykjavik Iceland Iceland Inst Intelligent Machines IS-102 Reykjavik Iceland Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9783031477201;9783031477218

Significant increases in industry requirements for network bandwidth are seen year after year. The exponential growth in streaming data is matched by an increase in the use of machine learning and deep learning to glean actionable insights from these data-ideally in real-time. Demand for artificial intelligence (AI) solutions to a variety of computational needs are likely to increase significantly over the coming years and decades. Meanwhile, the capacity of AI and data scientists to meet current requirements with contemporary approaches, which require continual updating and retraining, is falling short of industry demands for automation on dimensions of critical importance, including training speed, accuracy, trustworthiness, and explainability. In this paper we introduce a hybrid AI approach to computational intelligence which features new self-supervised learning mechanisms, a knowledge model engineered to include support for machine generated ontologies, as well as traditional human-generated ontologies, and interfaces to AGI systems such as OpenNARS, AERA, ONA, and OpenCog. Our hybrid AI system is capable of self-supervised learning of machine-generated ontologies from millions of time series, to provide real-time data-driven insights for large-scale deployments including data centers, enterprise networks, and video analytics. Preliminary results across all the use cases we have attempted to date are promising, but more work is needed to fully map out both the approach's benefits and limitations. This Hybrid AI project, and associated data, are expected to be available as open source in April 2023.

关键词： Artificial intelligence AI Hybrid AI Video analytics

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Real-Time Data Acquisition Based on IoT for Monitoring Autonomous Photovoltaic systems 4th

Real-Time Data Acquisition Based on IoT for Monitoring Auton...

引用

4th International Research and Innovation Congress - Smart Technologies and systems (CI3)

作者： Jacome, Fernando Osorio, Henry Daniel Andagoya-Alba, Luis Paredes, Edison Inst Super Univ Ruminahui Sangolqui Ecuador

ISBN: (纸本)9783031634338;9783031634345

Technological development has made photovoltaic systems more accessible for applications in domestic self-generation microsystems. In this context, new monitoring techniques are necessary to visualize the operating status of photovoltaic systems components. The main objective of this work is to monitor the operation status of a photovoltaic system through remote measurements in real-time using IoT technology. The study used IoT technology for the remote acquisition of principal elements of a photovoltaic system data to show them in time series through a graphical interface. The study resulted in a real-time remote monitoring system that allows the observation of photovoltaic system variables. The study concludes that through IoT technology, complex real-time monitoring systems of photovoltaic system operating variables can be configured to store them in a database that allows the visualization and analysis of historical data behavior of photovoltaic systems.

关键词： Photovoltaic System Remote Monitoring IoT Electrical Variables

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Modelling Legal Judgements with BDI Agents in Mercury: Analysis of the Pierson Versus Post Case 13th

Modelling Legal Judgements with BDI Agents in Mercury: Analy...

引用

13th Computer Science Online Conference

作者： Vidal-Lopez, Rodrigo Hernandez-Servin, J. A. Univ Autonoma Estado Mexico Toluca Mexico

ISBN: (纸本)9783031702846;9783031702853

This paper implements Belief-Desire-Intention operators in the Mercury Language based on Atkinson's formalism to simulate legal judgements, specifically analyzing the Pierson versus Post case. By showcasing how a BDI agent dynamically adapts its belief and desire system through arguments and attacks, the research underscores the critical adaptability required for legal reasoning. The interplay between law, computer science, and philosophy is examined, and technical challenges in legal decision-making automation. The purpose of the paper also aims to short the distance between the logical multiagent formalization and practical applications in artificial intelligence within the law.

关键词： Decision making

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Machine Learning-Based Demand Forecasting for an FMCG Retailer

Machine Learning-Based Demand Forecasting for an FMCG Retail...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Ceran, Berkan Ozkan, Ece Eskiocak, Defne Idil Mert, Buse Yuceoglu, Birol Migros Ticaret AS R&D Ctr TR-34758 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

Supply chain management is an important part of the retail industry. It helps to control inventory levels, logistics expenses while providing adequate service to consumers, especially in large retail companies. There are many steps to managing the supply chain. One of the most important steps is having an accurate demand forecasting. Due to the wide variety of products in the retail sector and the many variables that affect sales, it can be difficult to forecast demand by hand. This study employs a machine learning-based approach to predict daily sales for each product-store tuple, determining store demand for a 15-day horizon. Predictions are based on statistical measures of past sales, product prices, promotions and time-dependent variables. Within the scope of this study, we create various features and use algorithms such as Linear Regression, Random Forest and LightGBM. We present the results of these models and compare our results with other approaches using various metrics.

关键词： Demand Forecasting Machine Learning

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Finding the Flies in the Sky Using Advanced Deep Learning Algorithms 2nd

Finding the Flies in the Sky Using Advanced Deep Learning Al...

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2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Tokat, Mustafa Bedir, Sumeyra Rasheed, Jawad Istanbul Sabahattin Zaim Univ Dept Comp Engn Istanbul Turkiye

ISBN: (纸本)9783031628702;9783031628719

Image processing is a concept that allows us to perform operations on images by extracting meaningful features from them. Deep learning algorithms are frequently used in image processing. Although there are many algorithms for image classification, in this study, Convolutional Neural Network based deep learning algorithms VGG16, ResNet50 and YOLOv8 (You Only Look Once) are used. After traditional algorithms such as Support Vector Machine, Linear Regression, KNN or Decision Tree, deep learning algorithms have been developed that perform very well in image processing. As we can see from this study, we can say that the YOLO algorithm has recently gone one step further. In the dataset used, there are a total of 1359 images with and without drone images. The images were pretrained on imagenet and used. When we compare these fine-tuned algorithms, the accuracy of VGG16 was %92.74, ResNet50 was %91.06 and YOLOv8 was %95.4. It was determined that YOLOv8 performed better according to the dataset used.

关键词： Drone detection convolutional neural network VGG16 Resnet50 YOLOv8

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Intelligent Malaria Detection and Species Classification: A Case of Rwanda 9th

Intelligent Malaria Detection and Species Classification: A ...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Bogale, Yamlak Mukamakuza, Carine Pierette Tuyishimire, Emmanuel Carnegie Mellon Univ Africa Coll Engn Kigali Rwanda Univ Johannesburg Acad Comp Sci & Software Engn Johannesburg South Africa

ISBN: (纸本)9789819732982;9789819732999

Malaria is a disease caused by the Plasmodium parasite, transmitted through the bites of infected Anopheles mosquitoes. It is one of the deadliest global health issues that remains a challenge for many sub-Saharan African countries. To address this difficulty, an efficient digital approach for parasite detection and identification of species and life cycles is required. This paper presents a comprehensive case study that primarily focuses on the comparison of feature extraction methods done on blood smear images within the context of Faster R-CNN backbone models used to detect and identify malaria parasite species. To extract features, the blood smear images utilized in this research have been collected from Rwanda, as the considered sub-Saharan case study. The study investigates the performance of various feature extraction techniques aiming a comparison, in the context of the developing world.

关键词： Malaria Parasites Faster R-CNN Detection Rwanda Developing world

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