Sensor Based Target Tracking With Application to Autonomous Docking and Self-Reconfigurability

2020 ◽  
Author(s):  
Shubhdildeep S. Sohal ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Input Image ◽  
Sensor Data ◽  
Decision Making Process ◽  
Multiple Robots ◽  
Robot Interaction ◽  
Sensory Data ◽  
Additional Information ◽  
Use Of Data ◽  
Selective Interactions ◽  
Autonomous Docking

Abstract This paper presents a target detection technique, which combines a supervised learning model with sensor data to eliminate false positives for a given input image frame. Such a technique aids with selective docking procedures where multiple robots are present in the environment. Hence the sensor data provides additional information for this decision making process. Senor accuracy plays a crucial role when the motion of the robot is defined by the use of data recorded by its sensors. The uncertainties in the sensory data can cause misalignments due to poor calibration of the sensor, which can result in poor positioning of the robot relative to its target. Such misalignments can play a significant role where certain accuracy is desired. Therefore, it is necessary to minimize such misalignments to achieve certainty for the robot interaction with its target. The work proposed in this paper allows achieving such accuracy using a vision-based approach by eliminating all false occurrences leading to selective interactions with the target. The proposed methodology is validated using a self-reconfigurable mobile robot capable of hybrid Wheeled-Tracked mobility, as an application towards autonomous docking of mobile robotic modules.

scholarly journals Multimodal Sensor Data Fusion for Activity Recognition Using Filtered Classifier

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1262 ◽  
Author(s):  
Muhammad Razzaq ◽  
Ian Cleland ◽  
Chris Nugent ◽  
Sungyoung Lee
Keyword(s):  
Activity Recognition ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sensor Data ◽  
Test Dataset ◽  
Sensory Data ◽  
Multimodal Sensors ◽  
Imbalanced Class Distribution ◽  
Single Modality

Activity recognition (AR) is a subtask in pervasive computing and context-aware systems, which presents the physical state of human in real-time. These systems offer a new dimension to the widely spread applications by fusing recognized activities obtained from the raw sensory data generated by the obtrusive as well as unobtrusive revolutionary digital technologies. In recent years, an exponential growth has been observed for AR technologies and much literature exists focusing on applying machine learning algorithms on obtrusive single modality sensor devices. However, University of Jaén Ambient Intelligence (UJAmI), a Smart Lab in Spain has initiated a 1st UCAmI Cup challenge by sharing aforementioned varieties of the sensory data in order to recognize the human activities in the smart environment. This paper presents the fusion, both at the feature level and decision level for multimodal sensors by preprocessing and predicting the activities within the context of training and test datasets. Though it achieves 94% accuracy for training data and 47% accuracy for test data. However, this study further evaluates post-confusion matrix also and draws a conclusion for various discrepancies such as imbalanced class distribution within the training and test dataset. Additionally, this study also highlights challenges associated with the datasets for which, could improve further analysis.

Data Collection and Analysis for the Creation of a Digital Shadow During the Production of Thermoplastic Composite Layers in Unbonded Flexible Pipes

2018 ◽  
Author(s):  
Martin Schäkel ◽  
John McNab ◽  
Neville Dodds ◽  
Tido Peters ◽  
Henning Janssen ◽  
...  
Keyword(s):  
External Pressure ◽  
Thermoplastic Composite ◽  
Sensor Data ◽  
Consolidation Process ◽  
Specific Strength ◽  
Additional Information ◽  
Flexible Pipes ◽  
Potential Part ◽  
Path Dependent ◽  
Unbonded Flexible Pipes

Unbonded flexible pipes present a mature technology for the efficient recovery and transport of hydrocarbons offshore. The substitution of metallic reinforcement layers in the multi-layered structure by thermoplastic fiber-reinforced polymer (FRP) presents a solution for self-weight issues of especially long pipes, as FRP materials display high specific strength and modulus while being resistant to external pressure and corrosion. The production of these layers is automated by the laser-assisted tape winding process without the need of additional curing steps. During the manufacturing process, several data like process temperature and consolidation pressure are continuously monitored by non-contact sensors to ensure process stability without interfering in the consolidation process. To gain additional information about the temperature distribution within the multi-layered laminate, contact temperature sensors were introduced in the tape winding process. By this method the temperature of subjacent tapes can be assessed during the continued winding process. Additionally, this paper features a new approach of utilizing winding path data for relating the time-dependent sensor data to the exact position on the produced part. The visualization of path-dependent sensor data opens up possibilities of linking quality monitoring results to manufacturing insufficiencies and potential part defects.

scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

Central Language Hypothesis in the Decision-Making Process

2016 ◽  
pp. 66-83 ◽  
Author(s):  
Duygu Buğa
Keyword(s):  
Decision Making ◽  
Theoretical Framework ◽  
Future Research ◽  
Limbic Cortex ◽  
Decision Making Process ◽  
Discussion Section ◽  
Research Directions ◽  
Additional Information ◽  
Future Research Directions ◽  
The Brain

The purpose of this chapter is to explore the potential connection between neuroeconomics and the Central Language Hypothesis (CLH) which refers to the language placed within the subconscious mind of an individual. The CLH forwards that in the brains of bilingual and multilingual people, one language is more suppressive as it dominates reflexes, emotions, and senses. This central language (CL) is located at the centre of the limbic cortex of the brain. Therefore, when there is a stimulus on the limbic cortex (e.g., fear, anxiety, sadness), the brain produces the central language. The chapter begins with an Introduction followed by a Theoretical Framework. The next section discusses the neurolinguistic projection of the central language and includes the survey and the results used in this study. The Discussion section provides additional information regarding the questionnaire and the CLH, followed by Future Research Directions, Implications, and finally the Conclusion.

Architecting IoT based Healthcare Systems Using Machine Learning Algorithms

2020 ◽  
pp. 40-66
Author(s):  
G. S. Karthick ◽  
P. B. Pankajavalli
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Machine Learning Algorithms ◽  
Daily Activities ◽  
Sensor Data ◽  
Time Data ◽  
Sensory Data ◽  
Hybrid Classification ◽  
Smart Data

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.

scholarly journals Big Data Revisited: A Rejoinder

2015 ◽  
Vol 30 (1) ◽  
pp. 70-74 ◽  
Author(s):  
Jannis Kallinikos ◽  
Ioanna D Constantiou
Keyword(s):  
Big Data ◽  
Final Section ◽  
Current Data ◽  
Data Capture ◽  
Sensor Data ◽  
Data Generation ◽  
Social Data ◽  
Use Of Data ◽  
Key Issues ◽  
Communicative Processes

We elaborate on key issues of our paper New games, new rules: big data and the changing context of strategy as a means of addressing some of the concerns raised by the paper's commentators. We initially deal with the issue of social data and the role it plays in the current data revolution. The massive involvement of lay publics as instrumented by social media breaks with the strong expert cultures that have underlain the production and use of data in modern organizations. It also sets apart the interactive and communicative processes by which social data is produced from sensor data and the technological recording of facts. We further discuss the significance of the very mechanisms by which big data is produced as distinct from the very attributes of big data, often discussed in the literature. In the final section of the paper, we qualify the alleged importance of algorithms and claim that the structures of data capture and the architectures in which data generation is embedded are fundamental to the phenomenon of big data.

scholarly journals Image processing technique in the study of cell structures of biomedical data

2019 ◽  
pp. 63-66
Author(s):  
В.В. Ляшенко ◽  
O.A. Кобилін ◽  
O.I. Рязанцев ◽  
I.O. Рязанцев
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Cellular Structures ◽  
Biomedical Data ◽  
Processing Methods ◽  
Additional Information ◽  
Cell Structures ◽  
Different Color

Image processing methods are used in all areas of research. These methods provide additional information, a better understanding of the object that is being studied. Among the areas of using image processing methods, medicine occupies a special place. Biomedical data allow us to assess human health, to identify diseases in the early stages. Images of cellular structures of cytological preparations are one of the examples of biomedical data. Based on image analysis methods, we can isolate various components of cellular structures of cytological preparations. To do this, we apply the methods of wavelet analysis for different color components of the input image. Applying morphological analysis, we can identify individual cellular structures. The results are shown on the example of images of cellular structures of cytological preparations.

scholarly journals Visibility of resources and assets in the shipyard through industrial internet of things

2021 ◽  
Vol 11 (2) ◽  
pp. 88-101
Author(s):  
Ibrahim Cil ◽  
Fahri Arisoy ◽  
Hilal Kilinc
Keyword(s):  
Internet Of Things ◽  
Transformation Process ◽  
Digital Transformation ◽  
Sensor Data ◽  
Cyber Physical System ◽  
Industrial Internet Of Things ◽  
Use Of Data ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Other Information

Industrial Internet of Things is becoming one of the fundamental technologies with the potential to be widely used in shipyards as in other industries to increase information visibility. This article aims to analyze how to develop an industrial IoT-enabled system that provides visibility and tracking of assets at SEDEF Shipyard, which is in the digital transformation process. The research made use of data from previous studies and by using content analysis, the findings were discussed. Industrial IoT enables the collection and analysis of data for more informed decisions.  Based on the findings, sensor data in the shipyard are transmitted to the cloud via connected networks. These data are analysed and combined with other information and presented to the stakeholders. Industrial IoT enables this data flow and monitors processes remotely and gives the ability to quickly change plans as needed. Keywords: Shipyard, Industrial Internet of Things, Cyber-Physical System, Visibility, Assets tracking;        

On the Attempt to Implement Social Addressability within a Robotic System

i-com ◽  
2017 ◽  
Vol 16 (2) ◽  
pp. 71-85
Author(s):  
Philipp Graf ◽  
Manuela Marquardt ◽  
Diego Compagna
Keyword(s):  
Experimental Approach ◽  
Human Robot Interaction ◽  
Light Conditions ◽  
Robot System ◽  
Robot Interaction ◽  
Additional Information ◽  
The Status ◽  
User Friendly ◽  
To Receive ◽  
Insight Into

AbstractWe conducted a Human-Robot Interaction (HRI) study during a science event, using a mixed method experimental approach with quantitative and qualitative data (adapted version of Godspeed Questionnaire and audio-visual material analysed videographically). The main purpose of the research was to gather insight into the relevance of the so-called “point of interaction” for a successful and user-friendly interaction with a non-anthropomorphic robot. We elaborate on this concept with reference to sociological theories under the heading of “addressability” and “social address” and generate hypotheses informed by former research and theoretical reflections. We implement an interface on our robot system, comprising two LEDs, which indicate the status of the robot/interaction, and which might possibly serve as basal form of embodied social address. In one experimental condition, the movements were accompanied by a light choreography, the other one was conducted without the LEDs. Our findings suggest a potential relevance of social address for the interaction partner to receive additional information, especially if the situation is a contingent one. Nevertheless, the overall rating on the Godspeed scales showed no significant differences between the light conditions. Several possible reasons for this are discussed. Limitations and advantages are pointed out in the conclusion.

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