The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation...
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The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality of data reception that allows reliable interpretation of what the UGV perceives in a given environment, as well as the use these data to control the UGV's navigation. This article aims to study different PID control algorithms to enable autonomous navigation on a robotic platform. The robotic platform consists of a forestry tractor, used for forest cleaning tasks, which was converted into a UGV through the integration of sensors. Using sensor data, the UGV's position and orientation are obtained and utilized for navigation by inputting these data into a PID control algorithm. The correct choice of PID control algorithm involved the study, analysis, and implementation of different controllers, leading to the conclusion that the Vector Field control algorithm demonstrated better performance compared to the others studied and implemented in this paper.
Securing transportation services is crucial for supply chain management (SCM), as it constitutes the most significant expense within this domain. Implementing an effective bid generation strategy in combinatorial auct...
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Securing transportation services is crucial for supply chain management (SCM), as it constitutes the most significant expense within this domain. Implementing an effective bid generation strategy in combinatorial auctions (CAs) as a procurement mechanism to secure these services is vital for enhancing SCM efficiency. This study presents an advanced approach for quantifying synergy within the transportation network, aiming to identify the optimal bundles of auctioned contracts (AC). An efficient synergy-based bid generation algorithm has been developed to determine the optimal bids to be submitted within a transportation CA framework. This approach addresses two scenarios: one that considers service time limitations and another that allows for flexibility in early time constraints for the AC. The study investigates the potential of implementing a discount system by achieving savings through a relaxed time approach. It also examines the system's effectiveness in promoting more efficient transportation by minimizing travel distances on roadways in relaxed early-time cases. This research provides an efficient solution to the bid generation problem (BGP) with high dimensionality. Results indicate that the BGP having 550 booked contracts (BC), 800 AC, and 220 cities is solved within a reasonable timeframe. Furthermore, the study found that the computational complexity of the synergy-based BGP relies more on the ratio of the number of AC to BC rather than solely on the number of AC.
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ISBN:
(数字)9781611978391
ISBN:
(纸本)9781611978384
Get 10% off by pre-ordering this book.
This item has not yet published. Pre-order now and we will ship and process payment when the book becomes available.
Most books on algorithms are narrowly focused on a single field of application. This unique book cuts across discipline boundaries, exposing readers to the most successful algorithms from a variety of fields. Algorithm derivation is a legitimate branch of the mathematical sciences driven by hardware advances and the demands of many scientific fields. The best algorithms are undergirded by beautiful mathematics. This book enables readers to look under the hood and understand how some basic algorithms operate and how to assemble complex algorithms from simpler building blocks.
Since publication of the first edition of algorithms from THE BOOK, the number of new algorithms has swelled exponentially, with the fields of neural net modeling and natural language processing leading the way. These developments warranted the addition of a new chapter on automatic differentiation and its applications to neural net modeling. The second edition also corrects previous errors, clarifies explanations, adds worked exercises, and introduces new algorithms in existing chapters.
In algorithms from THE BOOK, Second Edition
the majority of algorithms are accompanied by Julia code for experimentation,
the many classroom-tested exercises at the end of each chapter make the material sui
Artificial intelligence(AI) is advancing airway management applications, especially for airway assessment, clinical decision support, and training. Traditional assessment methods struggle with time and precision as co...
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Artificial intelligence(AI) is advancing airway management applications, especially for airway assessment, clinical decision support, and training. Traditional assessment methods struggle with time and precision as complex airway disorders become more common. AI's powerful data processing and pattern recognition capabilities can assess patient imaging and clinical characteristics using deep learning algorithms to predict airway complications. In dynamic clinical environments, AI-assisted management solutions can improve airway control safety and efficiency by providing unique decision support. Additionally, AI systems using virtual reality and simulation training technologies can customize training programs for healthcare professionals based on airway difficulty, improving learning curves and clinical competencies in complex airway scenarios. AI in airway management shows its potential in assessment, clinical decision-making, and medical education. In clinical applications, we must also weigh AI's advantages and disadvantages. This review examines AI technology's current uses, future potential, and limitations in clinical practice and medical education.
PurposeThis paper considers the implications of not knowing - hypocognition - the lack of a cognitive or linguistic representation of a concept, algorithms, held by librarians responsible for programs of information l...
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PurposeThis paper considers the implications of not knowing - hypocognition - the lack of a cognitive or linguistic representation of a concept, algorithms, held by librarians responsible for programs of information literacy in universities in NSW, ***/methodology/approachA practice-based study of university librarians and their role in the development of algorithmic literacy, using semi-structured interviews and thematic analysis, showed that they had limited socio-technical knowledge of *** knowing led most participants to anthropomorphise algorithms, including those found in search engines such as Google, sometimes explaining them as something mysterious, although they were aware that the algorithms were gathering data about them and their online interactions. Nonetheless, they delegated responsibility for online activities. These online interactions were not presented in system terms, but often could be interpreted as examples of Goffman's civil inattention, a social norm used in interactions with strangers, such as fellow passengers. Such an understanding prevented the development of robust algorithmic *** implicationsWith technologies disrupting social norms, algorithms cannot be considered strangers who understand such civility;instead, metaphorically and practically, they rudely rummage through wallets and phones. Acknowledging the implications of the reliance on socio-cultural understandings of algorithms and their anthropomorphic representations for explaining online system-based interactions can present new ways for developing algorithmic ***/valueThis study suggests that the links between hypocognition and the anthropomorphising of algorithms can undermine the development of knowledge and skills in information and digital literacies.
The results of the stepwise computations are saved in memory slots, for possible reuse. The extended calculus reduces unnecessary iterations of assignments, which are chains of unnecessary assignments, by copying valu...
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ISBN:
(纸本)9783031820724;9783031820731
The results of the stepwise computations are saved in memory slots, for possible reuse. The extended calculus reduces unnecessary iterations of assignments, which are chains of unnecessary assignments, by copying values of terms from one memory slot to another, without any essential algorithmic changes. The primary applications of the chain-free type theory of recursion are for computational semantics of formal and natural languages, including programming languages and compilers.
We created and field-tested in-class activities that target mathematical underpinnings of algorithms such as proofs of correctness, design of counterexamples for incorrect algorithms, and design of (correct) new algor...
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ISBN:
(纸本)9798400705311
We created and field-tested in-class activities that target mathematical underpinnings of algorithms such as proofs of correctness, design of counterexamples for incorrect algorithms, and design of (correct) new algorithms. These challenging topics are difficult for the students to grasp, and this issue is further exacerbated by students' varied mathematical background and classes with large enrollments. Active learning has become very popular in introductory courses, but materials for algorithms appear to be lacking. We are aware of existing activities that ask students to trace a standard algorithm, which helps them to understand how that algorithm works, but often not why. Our activities target the "why" aspect: the algorithmic critical thinking. We deployed our activities in two classes: a large class of 108 students and a small honors section of 8 students. We describe our activities, the rationale behind them, and our impressions and students' feedback related to these activities.
This short article critically examines the growing body of research on algorithms and platforms in Latin America. Drawing on an analysis of articles published in Portuguese or Spanish in journals of the region, we dis...
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This short article critically examines the growing body of research on algorithms and platforms in Latin America. Drawing on an analysis of articles published in Portuguese or Spanish in journals of the region, we discuss the main analytical approaches that shape Latin American platformization research. We reveal a tension between two dominant analytical orientations. On one hand, there is a tendency to directly apply concepts and theories from the global North to interpret Latin American experiences, a phenomenon we call "tropicalization." On the other hand, research from the region also produces context-specific knowledge that questions established theoretical frameworks or bridges the study of the local and the global. While both orientations are valuable, we emphasize the importance of developing analytical approaches tailored to address the specific realities of Latin America. These approaches underscore the significance of regionally grounded scholarship in understanding contemporary cultural practices.
While studies often explore the intended and unintended consequences of technologies, few have theorized how and why they change. One crucial transformation in quantitative technologies is the shift from evaluative ac...
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While studies often explore the intended and unintended consequences of technologies, few have theorized how and why they change. One crucial transformation in quantitative technologies is the shift from evaluative accountability to predictive algorithms, such as in schools that use dropout prediction systems. Using the case of ninth-grade early warning indicators, I argue that the transformation of quantification resulted from interorganizational learning, or the acquisition of new knowledge through the interaction of different organizations. In particular, I show how technology changes gradually from organization-level evaluation to individual-based prediction to systems-focused improvement. Pivotal to such changes were new forms of knowledge that emerged (1) as "instructing" organizations directed changes and "receiving" organizations resisted them;(2) as organizations in various fields reciprocally collaborated;and (3) as similar organizations practiced networked learning. Although studies have traditionally highlighted the "discipline" of technologies, I illustrate the power of organizational agents to resist, adapt, and change them-with implications for the study of quantification, work, institutional change, and education.
In this paper, machine learning algorithms have used to estimate the length of stay (LOS) of subjects with lower limbs fractures. The dataset used comes from the A.O. Federico II based in Naples and includes 132 patie...
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ISBN:
(纸本)9783031803543;9783031803550
In this paper, machine learning algorithms have used to estimate the length of stay (LOS) of subjects with lower limbs fractures. The dataset used comes from the A.O. Federico II based in Naples and includes 132 patients. KNIME software were used to predict LOS using different type of Machine Learning (ML) classification models. The input variables were sex, age, the presence of comorbidities (0/1), the hospitalization regime (1-5) and mode of discharge (1/2). The very best result was attained with SVM and LR algorithms with an accuracy of 77.78%. These values obtained were compared with those coming from the analysis of data obtained from two other Hospital ("San Giovanni di Dio and Ruggi d'Aragona" of Salerno and at the "A.O.R.N. Antonio Cardarelli" of Naples) to evaluate the difference from the hospitals. For the future development, using the same algorithms, the best efficiency is obtained with the largest dataset.
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