Advances in Intelligent Systems Research and Innovation

Contents
Balancing Exploration and Exploitation in Forward Model Learning
1 Introduction
2 Taxonomy of Learning Algorithms
3 Forward Model Learning
4 Forward Model Representation
4.1 Model Building Heuristics
5 Improving the Confidence of a Forward Model
5.1 Measuring the Learned Model's Confidence
5.2 Learning Goals Based on the Model's Confidence
6 Evaluating the Agent's Performance
6.1 Motion Control Environments
6.2 Experiment Setup—Agent Performance
6.3 Results
6.4 Discussion
7 Evaluating the Effects of Confidence-Based Sampling
7.1 Experiment Setup—Training Efficiency
7.2 Results
8 Conclusion and Future Work
References
Universal Adversarial Perturbation Generated by Using Attention Information
1 Introduction
2 Adversarial Perturbation for Deep Neural Networks
3 Universal Adversarial Perturbations for Deep Neural Networks
4 Interpretation Methods for Deep Neural Networks
5 Universal Adversarial Perturbation Generated by Attention
5.1 Layer-Wise Relevance Propagation
5.2 Attack Method
6 Experiment Results
6.1 Dataset
6.2 Pre-trained Model
6.3 Training Setup
6.4 Fooling Ratio
6.5 Visualization of Perturbations and Effect
6.6 Transferability
7 Conclusion
References
Framework and Development Process for IoT Data Gathering
1 Introduction
2 Related Research
2.1 Development Process of the Prototypes
2.2 The Framework for Prototyping
3 Descriptive Model for the Prototyping Process (DMPP)
4 SW/HW Framework for IoT Data Gathering
4.1 Hardware of the SW/HW Framework
4.2 Software of the SW/HW Framework
5 Validating the SW/HW Framework by Prototyping
5.1 Type 1 and Type 2 with SBC Related Prototype Systems
5.2 Type 2 with SBC Related Prototype Systems
5.3 Type 3: Smartphone Related Prototype Systems
6 Discussion
7 Summary
References
Automated Environmental Mapping with Behavior Trees
1 Introduction
2 Related Work
3 Environmental Measuring and Mapping
3.1 Robot Localization
3.2 Measurement Server
3.3 3D Mapping
3.4 Measurement Post-processing
4 Exploration Path Planning
4.1 Complete Coverage Path Planning
4.2 Planning Using Contours
5 Behavior Trees (BTs)
5.1 Fundamentals and Tools
5.2 Integration in Robotic Systems
5.3 Automated Exploration Using BTs
5.4 Robot Navigation Using BTs
6 Performance at the EnRicH
7 Discussion
References
Multi-objective Automatic Clustering with Gene Rearrangement and Cluster Merging
1 Introduction
2 Related Work
2.1 Multi-objective Metaheuristics
2.2 Genetic Algorithms for Automatic Clustering
2.3 Clustering Validity Indexes
3 Proposed Approach
3.1 Objective Function
3.2 Chromosome Representation
3.3 Individual Fitness Calculation
3.4 Non-dominated Sorting and Population Selection
3.5 Gene Crossover and Gene Rearrangement
3.6 Gene Mutation and Chromosome Variation
3.7 Invalid Chromosome Check and Cluster Merge
3.8 Best Solutions Recommendation
4 Evaluation
4.1 Datasets
4.2 Experiments
5 Results
5.1 Automatic Clusters Number Finding
5.2 Partition Accuracy
5.3 Performance
5.4 Computational Time Complexity
6 Conclusion
References
Assessment of Deep Learning Models for Human Activity Recognition on Multi-variate Time Series Data and Non-targeted Adversarial Attack
1 Introduction
2 Related Work
2.1 Feature Engineering in the Field of Human Activity Recognition
2.2 Deep Neural Network for Time-Series Data
2.3 Adversarial Attack
3 Data Source
4 Methodology
4.1 Data Set Preprocessing (Normalization)
4.2 Accuracy Improvement by Deep Neural Network Based Classifier
4.3 Adversarial Attack on Pretrained Model
5 Performance Evaluation
5.1 Accuracy
5.2 Adversarial Attack
6 Discussion
6.1 Importance of Feature Engineering
6.2 Time-Series Data Classification
6.3 Dependency of Model Architecture on Nature of Data
6.4 Resiliency of Deep Learning Model When Confronted by Non-targeted Adversarial Attack
7 Future Work
8 Conclusion
References
Complex Multivalued Hierarchical Logic (HS-Logic)
1 Introduction
2 Complex Hierarchical Multivalued Logic
3 Conclusion
References
Design Considerations for a Multiple Smart-Device-Based Personalized Learning Environment
1 Introduction
2 Related Works
2.1 Estimating Individual Learner States During Learning
2.2 Personalized or Adaptive Learning Services
3 Multiple Smart-Device-Based Learning Environments
3.1 Overview
3.2 System Components and Function
4 Prototype
4.1 System Objective
4.2 System Implementation
4.3 Experimental Scenario
4.4 Experimental Result
5 Considerations for System Design
5.1 Hierarchical Clustering Analysis
5.2 Effect of Learner’s Reading Behavior
5.3 Effect of Learner’s Physical Behavior
6 Conclusions
References
Intelligent Assistive Sensors and Smart Systems for the Control and Analysis of Driver Reaction Times
1 Introduction
2 Wheelchair Systems
2.1 Joystick
3 Intelligent System
4 Testing
5 Results
5.1 Comparison When Using Sensors to Assist and Without Any Sensors
5.2 Time Taken to Complete Test Runs
5.3 Failures
6 Discussion and Conclusions
References
Implementation and Experimental Validation of the IEC 63047 Standard for Data Transfer in Radiological and Nuclear Robotic Applications
1 Introduction
2 Background
2.1 Benefits and Challenges of Robotics
2.2 Benefits and Challenges of List-Mode Data Acquisition
3 IEC 63047 for List-Mode Data Acquisition
3.1 IEC 63047 in Comparison to Other Data Formats
3.2 ASN.1-Based Specification
3.3 Exemplary Open Source Implementation
4 Technical Approach
4.1 Preliminary Tests
4.2 Integration into the Robot Operating System (ROS)
4.3 Encoding and Bandwidth Tests
5 System Test at the European Robotics Hackathon 2019
6 Conclusion and Outlook
6.1 Upcoming Multi-robot Multi-sensor Tests
References
Data Science Modeling and Constraint-Based Data Selection for EEG Signals Denoising Using Wavelet Transforms
1 Introduction
2 Deep Knowledge Modeling and Its Applications
3 Electroencephalography (EEG) Applications
3.1 Electroencephalography
3.2 Signal and Data Processing of EEG
3.3 Applications of EEG
4 EEG Signal Denoising Using Wavelets
4.1 Wavelet Transform
4.2 Wavelet Denoising Algorithm
5 Analysis of Variance (ANOVA)
6 Results
7 Conclusions
References
Intuitionistic Fuzziness, Standard and Extended Modality
1 Introduction
2 Preliminaries
3 Definitions of Four New Standard Intuitionistic Fuzzy Modal Operators
4 Definitions of Two New Extended Intuitionistic Fuzzy Modal Operators
5 Conclusion, or Discussion on the Basic Idea of the Modality
References
Introduction to Octopus-Inspired Soft Robots: Pipe-Climbing Robot TAOYAKA-S II and Ladder-Climbing Robot MAMEYAKA
1 Introduction
2 Problem of a Conventional Framework and the Goal of This Study
3 Behavior of an Octopus
4 Proposed Leg Mechanism
5 Pipe Climbing Robot
5.1 Flexible Leg
5.2 Trunk
5.3 Actuation Mechanism and Control
5.4 Experimental Verification
6 Ladder Climbing Robot
6.1 Grasping Part
6.2 Lifting Part
6.3 Actuation Mechanism and Control
6.4 Experimental Verification
7 Conclusion
References
Automatic Log Analysis to Prevent Cyber Attacks
1 Introduction
2 Related Work
3 Data
4 Feature Selection
4.1 Optimal Feature Selection
4.2 Factor Analysis
5 Cyber Attacks Predictive Models
5.1 Decision Tree
5.2 K-Nearest Neighbours
5.3 Neural Network
5.4 Naive Bayes
5.5 Model Evaluation
6 Conclusions
References
Generalized Net Model for Collecting, Evaluating and Including of Facts in the Educational Content
1 Introduction
2 A Generalized Net Model
3 Conclusions
References
Virtualization of Things in a Smart Agriculture Space
1 Introduction
2 Related Works
3 Brief Overview of ViPS
4 A Smart Agriculture Space
5 Virtualization of the Physical “Things”
5.1 Modelling of Things as Ambients
5.2 Modelling of Things as Artifacts
6 Demonstration Scenario
6.1 Ambient-Oriented Modeling in AmbiNet
6.2 Agent- and Artifact-Oriented Modelling
7 Conclusion
References
Turbine Hill Chart Generation Using Artificial Neural Network
1 Introduction
2 Hydropower Turbine and Hill Chart
3 Artificial Neural Networks
4 The Dataset
5 ANN-FF Proposed Model
5.1 Evaluation of ANN-FF Models
6 Results and Discussion
6.1 ANN-FF for Hill Chart Generation
6.2 ANN-FF for Efficiency Estimation
7 Conclusion
References
Stimuli-Based Control of Negative Emotions in a Digital Learning Environment
1 Introduction
2 Characteristics of Emotions in the Context of a Learning Element
2.1 Basic Emotions
2.2 Emotion Stimuli
2.3 Emotion Recognition
2.4 Mathematical Models of Emotions
2.5 Uncertainties and Limitations of Correct Estimation of the Stimuli—Emotional State Relations
2.6 Emotion Integration
3 Computer-Based Learning Systems
3.1 Learning Process
3.2 Personalization of the Learning Process
4 Learning Context-Based Framework of Emotional Regulation
5 Control Problem Formulation
6 Causality-Based Regulation of Emotions in an Advanced Learning System
7 Integrated Learning/Emotion Control System
8 Decreasing Learning-Based Emotion EmL in the Integrated Intelligent Control System
9 Conclusion
References
Intelligent Network-Flow Solutions with Risks at Transportation of Products
1 Introduction
2 Problem Formalization and Denotations
2.1 Numerical Example
3 Circulation at Transportation
3.1 Numerical Examples for Circulation
4 Conclusion
References
Embedded Intelligence in a System for Automatic Test Generation for Smoothly Digital Transformation in Higher Education
1 Introduction
2 Prerequisites for Authomatic Test Generation
2.1 System for Automatic Generation of e-Tests
2.2 Technologies that Brings Intelligence into Systems. Literature Review
2.3 Digital Transformation in Higher Education
3 Methodology
3.1 Algorithm with Fixed Number of Questions (Fixed Algorithm)
3.2 Algorithm with Dynamic Determination of the Number of Questions (Dynamic Algorithm)
3.3 Key Performance Indicators for e-Testing
4 Results and Discussion
4.1 Results of the Experiment
4.2 Discussion
4.3 Limitations of the System with Embedded Intelligence
5 Conclusion
References
Simulation Algorithms to Assess the Impact of Aging on the Reliability of Standby Systems with Switching Failures
1 Introduction
2 Analytical Solutions Without Aging Assumptions
3 Numerical Solution with a Full Aging Assumption
4 Simulation Solution with a Full Aging Assumption
5 Conditional and Unconditional Sampling
5.1 Inverse Transformation Sampling from a Reliability Function
5.2 Inverse Transformation Sampling from a CDF
5.3 Numerical Problems in the Implementation of Alg C
6 Aggregated Algorithm For Simulation Solution of a Full Aging System
7 Simulation Solution with Partial Aging and No Aging
7.1 2SBSF with No Aging
7.2 2SBSF with Partial Aging
8 Numerical Examples
8.1 Numerical Example Group 1
8.2 Numerical Example Group 2
9 Conclusions
References