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Beginners : the joy and transformative power of lifelong Learning
\"The best-selling author of Traffic and You May Also Like now gives us a thought-provoking, playful investigation into the transformative joys that come with starting something new, no matter your age\"-- Provided by publisher.
Highly Thermally Conductive Adhesion Elastomer Enhanced by Vertically Aligned Folded Graphene
Heat and stress transfer at an interface are crucial for the contact‐based tactile sensing to measure the temperature, morphology, and modulus. However, fabricating a smart sensing material that combines high thermal conductivity, elasticity, and good adhesion is challenging. In this study, a composite is fabricated using a directional template of vertically aligned folded graphene (VAFG) and a copolymer matrix of poly‐2‐[[(butylamino)carbonyl]oxy]ethyl ester and polydimethylsiloxane, vinyl‐end‐terminated polydimethylsiloxane (poly(PBAx‐ran‐PDMS)). With optimized chemical cross‐linking and supermolecular interactions, the poly(PBA‐ran‐PDMS)/VAFG exhibits high thermal conductivity (15.49 W m−1 K−1), an high elastic deformation, and an interfacial adhesion of up to 6500 N m−1. Poly(PBA‐ran‐PDMS)/VAFG is highly sensitive to temperature and pressure and demonstrates a self‐learning capacity for manipulator applications. The smart manipulator can distinguish and selectively capture unknown materials in the dark. Thermally conductive, elastic, and adhesive poly(PBA‐ran‐PDMS)/VAFG can be developed into core materials in intelligent soft robots. With optimized chemical cross‐linking and supermolecular interactions, the poly(PBA‐ran‐PDMS)/VAFG composite exhibits high thermal conductivity, elastic, and interfacial adhesion. Manipulator equipped with poly(PBA‐ran‐PDMS)/VAFG is highly sensitive to temperature and pressure, and demonstrates a self‐learning, distinguishing unknown materials in the dark.
Journal Article
Lean learning : how to achieve more by learning less
\"Navigate the chaos of information overload and supercharge your efficiency with Lean Learning, a groundbreaking guide that reveals a counterintuitive approach to success: winning by learning less. From an early age, we're taught that more is better. More money, more information, more skills. But times have changed. What was once valuable has now become a burden, and if information alone were the answer, we'd all be exactly where we want. In today's fast-moving world, the difference between success and failure is not in what you know but in what you do with what you know. Lean Learning equips you with the tools to do just that, propelling you towards your goals with greater efficiency, purpose, and results. Pat Flynn, a seasoned and serial entrepreneur and business mentor to millions, draws on his own experiences and of those who have successfully implemented his techniques. Lean Learning isn't just about absorbing information efficiently-it's about reshaping your approach to knowledge altogether. This book teaches you how to identify what's essential for your growth and eliminate all the distractions that tend to bog you down. Lean Learning stands out in a crowded productivity space by focusing not just on \"working smarter\" but on revolutionizing the way we absorb, process, and use information every single day. It's a perfect read for entrepreneurs, professionals, and lifelong learners who are ready to cut through the chaos and start making real progress. Backed by Flynn's extensive entrepreneurial success real-life case studies, Lean Learning offers a transformative approach to mastering any skill and achieving more with less. It is a perfect reading companion for fans of Great at Work, Digital Minimalism, and Building a Second Brain\"-- Provided by publisher.
BOOK
Self-Learning Model for Pattern Recognition in Vision System Based on Adaptive Kernel
Recently, the solution for recognizing and understanding an object based on visuals is to integrate the adaptation function (continuous machine-driven process) into the system update function involving humans (continuous human-driven process). However, this has created a gap between the adaptation function and the system. This situation requires understanding the system viewed as a dynamic composition of the learning process. This research introduced a self-learning model in the form of an adaptive kernel equipped with the SpinalNet architecture, and the goal of this study is to increase the Convolutional Neural Network (CNN) accuracy. The model consisted of a domain model, contextual knowledge, and adaptive learner developed based on the CNN with SpinalNet. The combination of Adaptive Kernel and SpiralNet in this CNN has a significant impact, allowing the model to adjust the selection of subsequent kernels based on the optimal input from the previous kernel. Moreover, this combination results in lower memory usage during training. The evaluation results show that our proposed model provides better classification accuracy than the SpiralNet model without the Adaptive Kernel. Furthermore, in terms of inference speed, our model outperforms SpiralNet, as evidenced by the use of fewer parameters
Journal Article
Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System
As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.
Journal Article
Quantum OPTICS and deep self-learning on swarm intelligence algorithms for Covid-19 emergency transportation
In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation.
Journal Article
Designing inclusive pathways with young adults : learning and development for a better world
This book is the second in a series entitled 'Learning and development for a better world' and it explores the potential for self-directed lifelong action learning (LAL) by focusing on the design of development pathways with and for young adults. The book considers the reasons why LAL pathways are needed and draws on innovative approaches used by the Global University for Lifelong Learning (including micro enterprise, peace-building, music, sport and the creative arts) with examples from nine countries. The aim is to offer a timely response to the pressing global problem of access to learning and development for marginalized young people during the vulnerable period from their mid-teens to mid-twenties.
Book
A Self-Learning Detection Method of Sybil Attack Based on LSTM for Electric Vehicles
Electric vehicles (EVs) are the development direction of new energy vehicles in the future. As an important part of the Internet of things (IOT) communication network, the charging pile is also facing severe challenges in information security. At present, most detection methods need a lot of prophetic data and too much human intervention, so they cannot do anything about unknown attacks. In this paper, a self-learning-based attack detection method is proposed, which makes training and prediction a closed-loop system according to a large number of false information packets broadcast to the communication network. Using long short-term memory (LSTM) neural network training to obtain the characteristics of traffic data changes in the time dimension, the unknown malicious behavior characteristics are self-extracted and self-learning, improving the detection efficiency and quality. In this paper, we take the Sybil attack in the car network as an example. The simulation results show that the proposed method can detect the Sybil early attack quickly and accurately.
Journal Article