scholarly journals Interspecies information systems

2021 ◽  
Author(s):  
Dirk van der Linden
Keyword(s):  
Information Systems ◽  
Information Flow ◽  
Health Monitoring ◽  
Data Driven ◽  
Design Engineering ◽  
New Class ◽  
Animal Data ◽  
Successful Design ◽  
Technical Systems ◽  
Cow Health

AbstractThis article introduces a new class of socio-technical systems, interspecies information systems (IIS) by describing several examples of these systems emerging through the use of commercially available data-driven animal-centered technology. When animal-centered technology, such as pet wearables, cow health monitoring, or even wildlife drones captures animal data and inform humans of actions to take towards animals, interspecies information systems emerge. I discuss the importance of understanding them as information systems rather than isolated technology or technology-mediated interactions, and propose a conceptual model capturing the key components and information flow of a general interspecies information system. I conclude by proposing multiple practical challenges that are faced in the successful design, engineering and use of any IIS where animal data informs human actions.

Designing for Trust

2009 ◽  
pp. 388-401 ◽  
Author(s):  
Piotr Cofta
Keyword(s):  
United Kingdom ◽  
Information Systems ◽  
Information Flow ◽  
Assessment Tool ◽  
Analytical Tool ◽  
Basic Principles ◽  
Technical Systems

Designing for trust is a methodology that attempts to design our perception of trust in information systems, in the long-term expectation that such systems will foster justified trust among people. The methodology contains several tools, but this chapter concentrates on a specific analytical tool that can be used to assess the compatibility between existing and required relationships of trust, in the context of information flow. While still under development, this methodology brings interesting results, identifying and addressing the strengths and weaknesses of incoming technical systems before they are actually deployed. This chapter discusses basic principles of designing for trust, presents the architectures of trust compatibility assessment tool and shows its applicability to citizen identity systems, using the proposed United Kingdom scheme as an example.

Privacy after the Agile Turn

2017 ◽  
Author(s):  
Seda Gurses ◽  
Joris Vredy Jan van Hoboken
Keyword(s):  
Information Systems ◽  
Software Industry ◽  
Data Driven ◽  
Dynamic Nature ◽  
Privacy Regulation ◽  
Research And Practice ◽  
Production Processes ◽  
Regulatory Frameworks ◽  
Software And Hardware ◽  
New Challenges

Moving beyond algorithms and big data as starting points for discussions about privacy, the authors of Privacy After the Agile Turn focus our attention on the new modes of production of information systems. Specifically, they look at three shifts that have transformed most of the software industry: software is now delivered as services, software and hardware have moved into the cloud and software’s development is ever more agile. These shifts have altered the conditions for privacy governance, and rendered the typical mental models underlying regulatory frameworks for information systems out-of-date. After 'the agile turn', modularity in production processes creates new challenges for allocating regulatory responsibility. Privacy implications of software are harder to address due to the dynamic nature of services and feature development, which undercuts extant privacy regulation that assumes a clear beginning and end of production processes. And the data-driven nature of services, beyond the prospect of monetization, has become part of software development itself. With their focus on production, the authors manage to place known challenges to privacy in a new light and create new avenues for privacy research and practice.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals Correction: Antanaitis, R., et al. Preliminary Experiment Using Sensors for Cow Health Monitoring after Surgical Treatment for the Left Displacement of the Abomasum. Sensors 2020, 20, 4416

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 918
Author(s):  
Ramūnas Antanaitis ◽  
Vida Juozaitienė ◽  
Mindaugas Televičius ◽  
Dovilė Malašauskienė ◽  
Mantvydas Merkis ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Health Monitoring ◽  
Cow Health

The authors wish to make the following corrections to this paper [...]

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

2021 ◽  
pp. 136943322110384
Author(s):  
Xingyu Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Frequency ◽  
Data Driven ◽  
Mode Shapes ◽  
Monitoring Methods ◽  
Destructive Testing ◽  
Electromechanical Impedance ◽  
Structural Health

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

scholarly journals Learning Damage Representations with Sequence-to-Sequence Models

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 452
Author(s):  
Qun Yang ◽  
Dejian Shen
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Research Community ◽  
Regions Of Interest ◽  
Data Driven ◽  
Economic Losses ◽  
Shake Table ◽  
Learning Models ◽  
Shake Table Tests ◽  
Damage States

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.

scholarly journals Data-driven filtration and segmentation of mesoscale neural dynamics

2021 ◽  
Author(s):  
Sydney C. Weiser ◽  
Brian R. Mullen ◽  
Desiderio Ascencio ◽  
James B. Ackman
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Background Noise ◽  
Activity Patterns ◽  
Data Driven ◽  
Neural Dynamics ◽  
Cortical Surface ◽  
Cerebral Function ◽  
Spatial Segmentation ◽  
Video Recordings

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis.

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