scholarly journals Interactive Smart Space for Single-Person Households Using Electroencephalogram through Fusion of Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7771
Author(s):  
Seung Yeul Ji
Keyword(s):  
Artificial Intelligence ◽  
Emotional State ◽  
Smart Space ◽  
Digital Twin ◽  
Model Driven ◽  
Implementation Model ◽  
Core Technology ◽  
Single Person ◽  
Eeg Data ◽  
Spatial Dimensions

The core technology for building a smart space includes the capability to analyse the space for users using various sensors. The purpose of this study was to propose a personalised interactive smart space implementation model driven by the fusion of digital twin (DT) and artificial intelligence (AI) based on electroencephalogram (EEG) data. This study utilised a handheld EEG sensor to identify a user’s emotion information and focused on the connection with the space. A smart space for single-person households that responds to EEG-based biometric information was designed for an interactive space that can improve the current emotional state of the space user. The technical characteristics of DT and AI were analysed to control spatial changes according to the user’s emotional state and to address safety-related issues. Furthermore, a fusion mechanism for DT and AI was developed for intelligent motor control to change the dimensions of the space in order to improve the EEG state of the user. In addition, using an AI model that converts EEG data into emotional state information, the user’s emotional state was analysed, and key issues related to the spatial dimensions and change of space that induce psychological stability were investigated.

Model-Driven Adaptive Enterprise

2020 ◽  
pp. 1-14
Author(s):  
Vinay Kulkarni ◽  
Sreedhar Reddy ◽  
Tony Clark
Keyword(s):  
Artificial Intelligence ◽  
Information Systems ◽  
Control Theory ◽  
A Priori ◽  
Systems Design ◽  
Dynamic Environments ◽  
Information Systems Design ◽  
Systems Of Systems ◽  
Digital Twin ◽  
Model Driven

Modern enterprises are large complex systems operating in dynamic environments and are therefore required to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is only available in localized contexts and is partial and uncertain. Given that the overall system behaviour is hard to know a-priori and that conventional techniques for systemwide analysis either lack rigour or are defeated by the scale of the problem, the current practice often exclusively relies on human expertise for adaptation. This chapter outlines the concept of model-driven adaptive enterprise that leverages principles from modeling, artificial intelligence, control theory, and information systems design leading to a knowledge-guided simulation-aided data-driven model-based evidence-backed approach to impart adaptability to enterprises. At the heart of a model-driven adaptive enterprise lies a digital twin (i.e., a simulatable digital replica of the enterprise). The authors discuss how the digital twin can be used to analyze, control, adapt, transform, and design enterprises.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Brandon Malone ◽  
Boris Simovski ◽  
Clément Moliné ◽  
Jun Cheng ◽  
Marius Gheorghe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Monte Carlo ◽  
Cell Surface ◽  
Antigen Presentation ◽  
Human Population ◽  
Host Immune System ◽  
T Cell Epitopes ◽  
Digital Twin ◽  
Novel Coronavirus ◽  
Global Population

AbstractThe global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

scholarly journals Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2338
Author(s):  
Sofia Agostinelli ◽  
Fabrizio Cumo ◽  
Giambattista Guidi ◽  
Claudio Tomazzoli
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Energy Management ◽  
Three Dimensional ◽  
Cost Effective ◽  
Automation System ◽  
Climate Conditions ◽  
Digital Twin ◽  
Building Energy Management ◽  
Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

MDE Intelligence 2021: 3rd Workshop on Artificial Intelligence and Model-Driven Engineering

2021 ◽  
Author(s):  
Loli Burgueno ◽  
Marouane Kessentini ◽  
Manuel Wimmer ◽  
Steffen Zschaler
Keyword(s):  
Artificial Intelligence ◽  
Model Driven Engineering ◽  
Model Driven

scholarly journals Application of Artificial Intelligence Technology in Mechanical Electrical Control System

2021 ◽  
Vol 2066 (1) ◽  
pp. 012057
Author(s):  
Nan Li
Keyword(s):  
Artificial Intelligence ◽  
Control System ◽  
Control Systems ◽  
Current Status ◽  
Work Efficiency ◽  
Electrical Control ◽  
Existing Problems ◽  
The Core ◽  
Core Technology ◽  
Artificial Intelligence Technology

Abstract Artificial intelligence technology (A I T) has also been widely used in society. Combining A I T with mechanical and electrical control systems will bring huge profits to the corporate sector and greatly improve work efficiency. It can save a lot of money in the electrical control operations of all walks of life in the country, and fill the gap in this technology in the country. The purpose of this article is to study the application of A I T in mechanical electrical control systems (M E C S). This article first introduces the basic theories and concepts of A I T, extends the core technology of A I T, and combines the current status of the electrical control system of modern enterprises in our country to discuss its existing problems, and finally studies and analyzes A I T and machinery. Combination of electrical control systems, and discuss the application of A I T in mechanical electrical orifice subsystems. Experiments show that, compared with the existing M E C S, the M E C S using A I T can better complete the work and improve work efficiency.

scholarly journals Engineering Emotionally Intelligent Agents

2011 ◽  
pp. 99-117
Author(s):  
Penny Baillie ◽  
Mark Toleman ◽  
Dickson Lukose
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Emotional State ◽  
Psychological Theory ◽  
Emotional States ◽  
Changing Environment ◽  
Emotionally Intelligent ◽  
Over Time ◽  
The Way

Interacting with intelligence in an ever-changing environment calls for exceptional performances from artificial beings. One mechanism explored to produce intuitive-like behavior in artificial intelligence applications is emotion. This chapter examines the engineering of a mechanism that synthesizes and processes an artificial agent’s internal emotional states: the Affective Space. Through use of the affective space, an agent can predict the effect certain behaviors will have on its emotional state and, in turn, decide how to behave. Furthermore, an agent can use the emotions produced from its behavior to update its beliefs about particular entities and events. This chapter explores the psychological theory used to structure the affective space, the way in which the strength of emotional states can be diminished over time, how emotions influence an agent’s perception, and the way in which an agent can migrate from one emotional state to another.

Using Digital Twin and Artificial Intelligence for asset integrity management: a smart tool for decision makers

2020 ◽  
Vol 20 (2020) ◽  
pp. 429-430
Author(s):  
Otavio Carneiro Correa ◽  
Jorge Luis Seleme Mariano ◽  
Fulvio Faria Silva ◽  
Marcos Cesar Gritti
Keyword(s):  
Artificial Intelligence ◽  
Decision Makers ◽  
Digital Twin ◽  
Integrity Management

Creating an AI-Conversational Agent in Psychotherapy: Recognizing User Intents

2021 ◽  
Author(s):  
Olga Troitskaya ◽  
Andrey Zakharov
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Interpersonal Relations ◽  
Emotional State ◽  
Conversational Agent ◽  
Intent Recognition ◽  
Self Help ◽  
User Query ◽  
Tree Algorithms ◽  
Commit Suicide

In recent years there has been a growth of psychological chatbots performing important functions from checking symptoms to providing psychoeducation and guiding self-help exercises. Technologically these chatbots are based on traditional decision-tree algorithms with limited keyword recognition. A key challenge to the development of conversational artificial intelligence is intent recognition or understanding the goal that the user wants to accomplish. The user query on psychological topic is often emotional, highly contextual and non goal-oriented, and therefore may contain vague, mixed or multiple intents. In this study we made an attempt to identify and categorize user intents with relation to psychological topics using the database of 43 000 messages from iCognito Anti-depression chatbot. We have identified 24 classes of user intents that can be grouped into larger categories, such as: a) intents to improve emotional state; b) intents to improve interpersonal relations; c) intents to improve physical condition; d) intents to solve practical problems; e) intents to make a decision; f) intents to harm oneself or commit suicide; g) intent to blame or criticize oneself. This classification may be used for the development of conversational artificial intelligence in the field of psychotherapy.

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