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Automata and computability : programmer's perspective
\"This class-tested textbook provides a comprehensive and accessible introduction to the theory of automata and computation. The author uses illustrations, engaging examples, and historical remarks to make the material interesting and relevant for students. It incorporates modern/handy ideas, such as derivative-based parsing and a Lambda reducer showing the universality of Lambda calculus. The book also shows how to sculpt automata by making the regular language conversion pipeline available through a simple command interface. A Jupyter notebook will accompany the book to feature code, YouTube videos, and other supplements to assist instructors and students\"-- Provided by publisher.
Book
Reinforcement Learning and Dynamic Programming Using Function Approximators
While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on continuous-variable problems, this unparalleled work provides an introduction to classical RL and DP, followed by a presentation of current methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, it offers illustrative examples that readers will be able to adapt to their own work.
eBook
A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets
With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).
Journal Article
Intelligent factory layout design framework through collaboration between optimization, simulation, and digital twin
In the era of the fourth industrial revolution, various internet and communications technologies (ICTs) are being applied to manufacturing systems. Based on these technologies, many companies utilize smart manufacturing systems to optimize the design and operation of their lines and to diagnose failures. To build and/or improve production lines, various computer-aided engineering (CAE) tools such as optimization solvers and simulation tools for validation are required. In addition, experts depend on their experience or utilize numerous trial and error processes, implying that a large time investment is required obtain the best layout design, without any guarantee that the result is in fact the best. Therefore, the paper proposes an integrated intelligent layout design framework that automatically derives an optimal layout according the requirements of the layout. The proposed framework uses mixed integer linear programming, simulation-based optimization, and digital twin to perform processes such as assembly line balancing, cell/buffer optimization, and layout planning sequentially and repeatedly to derive an optimal layout. By applying this, it is possible to automatically derive the optimal layout design considering limited resources and physical constraints. In addition, it can contribute to improving productivity and work efficiency at manufacturing sites.
Journal Article
Digital Twin and web services for robotic deburring in intelligent manufacturing
The development of modern manufacturing requires key solutions to enhance the intelligence of manufacturing such as digitalization, real-time monitoring, or simulation techniques. For smart robotic manufacturing, the modern approach regarding robot programming and process planning aims for both high efficiency and energy-awareness. During the design and manufacturing stages, optimization becomes crucial and can be fulfilled by means of appropriate digital manufacturing tools. This paper presents the development of a Digital Twin for a robotic deburring workcell along with the process planning and robot programming. Considering a large size workpiece, a new robot programming solution was implemented, based on image processing to safely re-machine only areas where burrs could not be completely removed in the main deburring routine. The work also covers the development of a new web platform to remotely monitor the robotic workcell, to trigger alerts for unexpected events and to allow the control to authorized personnel enabled by the employment of robot web services following an architectural RESTful style which establishes a communication link to the robot virtual controller. The aim of this research is to integrate the Digital Twin with the innovative proposals of Industry 4.0, offering a project-based model of smart robotic manufacturing and experience concepts such as Cyber-Physical System, digitalization, data acquisition, continuous monitoring, and intelligent solutions in a novel approach. Furthermore, the work covers energy consumption strategies for energy-aware robotic manufacturing. Finally, the results of an energy-efficient motion planning along with signal-based scheduling optimization of the robotic deburring cell are discussed.
Journal Article
Improving cloud storage and privacy security for digital twin based medical records
As digital transformation progresses across industries, digital twins have emerged as an important technology. In healthcare, digital twins are created by digitizing patient parameters, medical records, and treatment plans to enable personalized care, assist diagnosis, and improve planning. Data is core to digital twins, originating from physical and virtual entities as well as services. Once processed and integrated, data drives various components. Medical records are critical healthcare data but present unique challenges for digital twins. However, directly storing or encrypting medical records has issues. Plaintext risks privacy leaks while encryption hinders retrieval. To address this, we present a cloud-based solution combining post-quantum searchable encryption. Our system includes key generation using Physical Unable Functions (PUF). It encrypts medical records in cloud storage, verifies records using blockchain, and retrieves records via cloud. By integrating cloud encryption, blockchain verification and cloud retrieval, we propose a secure and efficient cloud-based medical records system for digital twins. Our implementation demonstrates the system provides users efficient and secure medical record services, compared to related designs. This highlights digital twins’ potential to transform healthcare through secure data-driven personalized care, diagnosis and planning.
Journal Article
Cybernetics in C++
C++ is a powerful, much sought after programming language, but can be daunting to work with, even for engineering professionals. Why is this book so useful? Have you ever wondered: • How do keywords like static and virtual change their meanings according to context? • What are the similarities and differences between Pointers and References, Pointers and Arrays, Constructors and Copy Constructors, Nested and Local Inner Classes? • Why is Multiple Interface Inheritance seen to be beautiful but Multiple Implementation Inheritance considered evil? • When is Polymorphism Static or Dynamic, Bounded or Unbounded? Answers on these questions, and much more, are explained in this book, Cybernetics in C++. What makes this text so different and appealing in comparison to existing books on the market? • The Bulleted style, as opposed to Prose, produces results much faster, both in learning and reference • Rules of Thumb, and further expert Tips are given throughout in how to optimise your code • The Prospective Evils sections tell you what to avoid • The thorough coverage ensures you will be trained to expert level in each of Imperative, Procedural, Memory & Resource Management, Object Oriented and Generic Programming Cybernetics in C++ combines a theoretical overview and practical approach in one book, which should prove to be a useful reference for computer scientists, software programmers, engineers and students in this and related field.
eBook
Mapping with ChatGPT
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. We conducted a pilot study exploring making maps with ChatGPT, a popular artificial intelligence (AI) chatbot. Specifically, we tested designing thematic maps using given or public geospatial data, as well as creating mental maps purely using textual descriptions of geographic space. We conclude that ChatGPT provides a useful alternative solution for mapping given its unique advantages, such as lowering the barrier to producing maps, boosting the efficiency of massive map production, and understanding geographical space with its spatial thinking capability. However, mapping with ChatGPT still has limitations at the current stage, such as its unequal benefits for different users and dependence on user intervention for quality control.
Journal Article
A Novel Medical Blockchain Model for Drug Supply Chain Integrity Management in a Smart Hospital
At present, in pharmacology one of the most serious problems is counterfeit drugs. The Health Research Funding organization reported that in developing countries, nearly 10–30% of the drugs are fake. Counterfeiting is not the main issue itself, but, rather, the fact that, as compared to traditional drugs, these counterfeit drugs produce different side effects to human health. According to WHO, around 30% of the total medicine sold in Africa, Asia, and Latin America is counterfeit. This is the major worldwide problem, and the situation is worse in developing countries, where one out of every 10 medicines are either fake or do not follow drug regulations. The rise of Internet pharmacies has made it more difficult to standardize drug safety. It is difficult to detect counterfeits because these drugs pass through different complex distributed networks, thus forming opportunities for counterfeits to enter the authentic supply chain. The safety of the pharmaceutical supply chain has become a major concern for public health, which is a collective process. In this paper, we propose a novel drug supply chain management using Hyperledger Fabric based on blockchain technology to handle secure drug supply chain records. The proposed system solves this problem by conducting drug record transactions on a blockchain to create a smart healthcare ecosystem with a drug supply chain. A smart contract is launched to give time-limited access to electronic drug records and also patient electronic health records. We also carried out a number of experiments in order to demonstrate the usability and efficiency of the designed platform. Finally, we used Hyperledger Caliper as a benchmarking tool to conduct the performance of the designed system in terms of transactions per second, transaction latency, and resource utilization.
Journal Article
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: local reference frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100, 98.9, 95.4 and 96.0 % respectively when tested on the Bologna, UWA, Queen’s and Ca’ Foscari Venezia Datasets.
Journal Article