Real-time Feedback Loop Research Articles

Optimizing Reinforcement

The combination of parametric modelling with structural analysis prompted new synergies between designing and engineering. The connection of structural calculation models such as Karamba3d to algorithmic modelling platforms such as Grasshopper3d allowed to embed tools for analysis and simulation into the environment of creative design processes. This way structural analysis advanced from its single sided calculation “duty” towards participation in design language of articulated structural expression. 3d-concrete-printing serves as a great new territory to apply the potential of real-time parametric structural analysis in the built environment of rapid prototyping and robotic fabrication. While 3d-printed-concrete rapidly advanced in technology and empiric know-how, so also grew the ambition to utilize them for larger purposes and bigger building projects. Known requirements such as structural integrity according to building code, interfaces for construction or waterproofing pose clear challenges for further realizations. In order to develop 3d-concrete-printing to its full potential all challenges must serve as opportunities to dissect and rethink established norms and practices, and to construct a new interdisciplinary rule book for an emerging building technology. In this field working through challenges via prototypes serve as great basis for development and allows for an applied discourse within the wider community. Furthermore to address challenges a flexible toolbox for structural analysis such as Karamba3d offers the potential to serve as an open instrument for structural analysis and to promote 3d-concrete-printing within that interdisciplinary effort to its next level. On the one hand the tool enables to suggest customized and optimized rebar layouts which could support the development of 3D-concrete-printing further. By enabling this more customizable calculation and tailored real time feedback for complex structures, solutions can develop more structurally informed. On the other hand, recent developments with Karamba3D within our office of Bollinger+Grohmann have shown the possibility of real time feedback loops between the physical printing process and the digital calculation model. Both developments show the potential that material systems can be optimized towards specific patterns or values of forces, or structures can be evaluated in real time for their load bearing capacity while hardening during printing.

A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.

Terra: A Smart and Sensible Digital Twin Framework for Robust Robot Deployment in Challenging Environments

Digital twin (DT) systems that replicate the physical world digitally are powerful tools for monitoring physical systems and evaluating algorithms, but current DT systems are commonly not applicable for robotic deployment and investigation. Meanwhile, current 3-D simulation-based robotic platforms do not model the dynamics of the physical world on-the-fly as done in DT systems, limiting their potential for the development of robotics in challenging environments. To tackle this issue, we propose the first robot-centered smart DT framework, namely, Terra, to facilitate the deployment of robots in challenging environments. The proposed Terra framework introduces a comprehensive DT representation to encode the useful real-time dynamics of both the physical world and the robot agent deployed therein. A multiview multimodality perception module is further devised for Terra to obtain high-level semantics and deliver a precise description of the current status of the environment and the robot agent. By mapping the perceived results to the virtual replica of the physical environment, Terra actively updates the action policy and sends it back to the agent, forming an integral and real-time information feedback loop. In practice, to help demonstrate the effectiveness and feasibility of the proposed framework, we deliberately set up a challenging unordered physical environment with many obstacles and a very simple robot aiming to fulfill a navigation task. Empirical results show that the proposed Terra framework successfully facilitates the robot to accomplish the task without causing hazards.

Publico – A Digital Tool for Pandemic Crisis Communication and Management

"COVID-19 vividly demonstrates the global challenges of crisis communication. A flood of pandemic-related information from various sources and highly variable quality is inundating media platforms. To counter this “infodemic”, providing high-quality information is not sufficient. Real-time feedback loops from the public to policy-makers are urgently needed to better align communication strategies, actions taken, and public perception. To achieve this, the PubliCo online platform was launched in November 2020 in Switzerland. It follows a transdisciplinary multi-stakeholder approach rooted in participatory citizen science. PubliCo (https://publico.community/en/) includes three parts: 1. PubliCo Survey, collecting quantitative data, e.g. on moral preferences, while feeding back contextualized, tailored information to users; 2. PubliCo Diaries, collecting in-depth qualitative data, which also informs efforts to adapt PubliCo Survey to the evolving situation; and 3. PubliCo Analytics, which allows policymakers and other platform users to continuously analyse the collected data. We will present the PubliCo concept, considering the following key points: 1. Ethical considerations in the development of PubliCo concerning citizen science and crisis management. 2. PubliCo as a practical tool to implement the procedural ethics concepts such as Accountability for Reasonableness. 3. Lessons learned challenges, and opportunities for the transferability of PubliCo to better manage future crises in different global contexts. With our contribution, we aim to simulate a discussion on the ethical aspects of crisis communication and the strengths and limitations of citizen participation. "

Embodied empathy: Using affective computing to incarnate human emotion and cognition in architecture

This research aims to develop a cyber-physical adaptive architectural space capable of real-time responses to people’s emotions, based on biological and neurological data. To achieve this goal, we integrated artificial intelligence (AI), wearable technology, sensory environments, and adaptive architecture to create an emotional bond between a space and its occupants and encourage affective emotional interactions between the two. The project’s objectives were to (1) measure and analyze biological and neurological data to detect emotions, (2) map and illustrate that emotional data, and (3) link occupants’ emotions and cognition to a built environment through a real-time emotive feedback loop. Using an interactive installation as a case study, this work examines the cognition-emotion-space interaction through changes in volume, color, and light as a means of emotional expression. It contributes to the current theory and practice of cyber-physical design and the role AI plays, as well as the interaction of technology and empathy.

Accelerated Deep Reinforcement Learning for Fast Feedback of Beam Dynamics at KARA

Coherent synchrotron radiation (CSR) is generated when the electron bunch length is in the order of the magnitude of the wavelength of the emitted radiation. The self-interaction of short electron bunches with their own electromagnetic fields changes the longitudinal beam dynamics significantly. Above a certain current threshold, the micro-bunching instability develops, characterized by the appearance of distinguishable substructures in the longitudinal phase space of the bunch. To stabilize the CSR emission, a real-time feedback control loop based on reinforcement learning (RL) is proposed. Informed by the available THz diagnostics, the feedback is designed to act on the radio frequency (RF) system of the storage ring to mitigate the micro-bunching dynamics. To satisfy low-latency requirements given by the longitudinal beam dynamics, the RL controller has been implemented on hardware (FPGA). In this article, a real-time feedback loop architecture and its performance is presented and compared with a software implementation using Keras-RL on CPU/GPU. The results obtained with the CSR simulation Inovesa demonstrate that the functionality of both platforms is equivalent. The training performance of the hardware implementation is similar to software solution, while it outperforms the Keras-RL implementation by an order of magnitude. The presented RL hardware controller is considered as an essential platform for the development of intelligent CSR control systems.

Coping with COVID: Developing a Rapid-cycle Frontline Quality-improvement Process to Support Employee Well-being and Drive Institutional Responsiveness in a Tertiary Care Faith-based Hospital in Rural Kenya.

ABSTRACT.The coronavirus disease 2019 (COVID-19) pandemic has demanded rapid institutional responses to meet the needs of patients and employees in the face of a serious new disease. To support the well-being of frontline staff, a series of debriefing sessions was used to drive a rapid-cycle quality-improvement process. The goals were to confidentially determine personal coping strategies used by staff, provide an opportunity for staff cross-learning, identify what staff needed most, and provide a real-time feedback loop for decision-makers to create rapid changes to support staff safety and coping. Data were collected via sticky notes on flip charts to protect confidentiality. Management reviewed the data daily. Institutional responses to problems identified during debrief sessions were tracked, visualized, addressed, and shared with staff. More than 10% of staff participated over a 2-week period. Feedback influenced institutional decisions to improve staff schedules, transportation, and COVID-19 training.

Successful Implementation of Enhanced Recovery After Surgery Program in a Safety-Net Hospital: Barriers and Facilitators

PurposeEnhanced recovery after surgery (ERAS) programs comprise bundles of evidence-based recommendations designed to reduce physiological stress and support early return of function after surgery. In this study, we sought to investigate the barriers and facilitators of successful implementation of ERAS in a major safety-net hospital. DesignOur ERAS program has been designed as a quality improvement pilot project in prospective fashion with a real-time feedback loop. The program is designed to address established culture of safety-net hospitals. MethodsAn extensive multidisciplinary team investigated the barriers to success for three different levels of program stakeholders: providers, patients, and the facility. After a systematic review of these barriers, solutions were offered and implemented in a multidisciplinary care model with special attention to outcomes and continuous feedback. The findings are summarized in a grid format for better understanding and implementation ease. FindingsPatients (N = 198) were enrolled in an ERAS program in a nonrandomized fashion during the pilot period of October 2017 to August 2018. ERAS cohort of patients’ outcomes were then compared with those of 20,328 non-ERAS patients. The ERAS group had less complication with shorter length of stay compared with their non-ERAS counterparts. Furthermore, it has cost less to take care of these patients. Interestingly, this decrease was not achieved by a reciprocal increase in subsequent readmission or reoperation rates. ConclusionsUnique barriers exist when implementing an ERAS protocol in a safety-net hospital. These barriers can be overcome to improve the quality of care at a decreased cost. We have provided a grid to facilitate the implementation process.

Diagnostic automated algorithms in neurodevelopmental disorders: Focus on automatic motor assessment

Difficulties in motor development are frequent and impairing. However, the assessment of these motor learning skills is difficult and limits early stage rehabilitation. Electronic sensors and algorithms can help to measure motor difficulties more easily and objectively. We will present a systematic review detailing these methods and challenges in Autism Spectrum Disorders (ASD). Electronic tablets, give access to handwriting features that are not usually evaluated in classical assessments. We describe how such digital features (in static, dynamic, pressure, and tilt domains) allow diagnosing dysgraphia and how they evolve during children development. From a finer analysis, three different clusters of dysgraphia emerge, longitudinal studies will allow to underline different patterns of development that seemingly require tailored remediation strategies. However, those digital features are not used in the context of conventional pen and paper therapies. It is possible to engage children with typical development in handwriting exercises by asking them to teach a robot to write. We implemented a long-term case study (20 sessions, 500 minutes in total) observing a child with severe Developmental Co-ordination Disorder who did not progress anymore with a classic pen and paper approach by enriching this setup with various training activities using real-time feedback loops (on tilt, pressure, dynamic, pauses). We show how this new method tackles previous child’s behaviour avoidances, boosting his motivation, and improving his motor and writing skills. This talk demonstrates how new motor digital features allow the implementation of innovative motor remediation interventions, which rely on fostering children’s personal characteristics and adaptation skills.DisclosureThe work presented in this talk was conducted during a PhD in computer science (funded by APHP and Paris 8 University, France). A company was funded by a PhD collaborator, dynamico.ch. The presenter does not have consultancy or financial connection with d

Real-time data assimilation and control on mechanical systems under uncertainties

This research work deals with the implementation of so-called Dynamic Data-Driven Application Systems (DDDAS) in structural mechanics activities. It aims at designing a real-time numerical feedback loop between a physical system of interest and its numerical simulator, so that (i) the simulation model is dynamically updated from sequential and in situ observations on the system; (ii) the system is appropriately driven and controlled in service using predictions given by the simulator. In order to build such a feedback loop and take various uncertainties into account, a suitable stochastic framework is considered for both data assimilation and control, with the propagation of these uncertainties from model updating up to command synthesis by using a specific and attractive sampling technique. Furthermore, reduced order modeling based on the Proper Generalized Decomposition (PGD) technique is used all along the process in order to reach the real-time constraint. This permits fast multi-query evaluations and predictions, by means of the parametrized physics-based model, in the online phase of the feedback loop. The control of a fusion welding process under various scenarios is considered to illustrate the proposed methodology and to assess the performance of the associated numerical architecture.

Public Opinions and Concerns Regarding the Canadian Prime Minister's Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques.

BackgroundDuring the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel.ObjectiveThe aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time.MethodsWe used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes.ResultsWe found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening.ConclusionsThis study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

Coherent Ising machines—Quantum optics and neural network Perspectives

A coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs), in which the “strongest” collective mode of oscillation at well above threshold corresponds to an optimum solution of a given Ising problem. When a pump rate or network coupling rate is increased from below to above threshold, however, the eigenvectors with the smallest eigenvalue of the Ising coupling matrix [Jij] appear near threshold and impede the machine to relax to true ground states. Two complementary approaches to attack this problem are described here. One approach is to utilize the squeezed/anti-squeezed vacuum noise of OPOs below threshold to produce coherent spreading over numerous local minima via quantum noise correlation, which could enable the machine to access either true ground states or excited states with eigen-energies close enough to that of ground states above threshold. The other approach is to implement a real-time error correction feedback loop so that the machine migrates from one local minimum to another during an explorative search for ground states. Finally, a set of qualitative analogies connecting the CIM and traditional computer science techniques are pointed out. In particular, belief propagation and survey propagation used in combinatorial optimization are touched upon.

Real-time parking slot availability for Bhavnagar, using statistical block matching approach

Purpose The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 × 10 and 20 × 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates “no parking space for that place.” Otherwise, the method yields “parking place available.” Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.

Development and application of an electronic treatment register: a system for enumerating populations and monitoring treatment during mass drug administration

ABSTRACT We developed an electronic treatment register for the DeWorm3 Project, a cluster-randomised, controlled trial in Benin, India, and Malawi testing the feasibility of interrupting transmission of soil-transmitted helminths through community-wide mass drug administration. The electronic treatment register was designed in xlsform, deployed via the SurveyCTO mobile data collection platform, and implemented on smartphones running the Android operating system. The versatile system enables collection of census and treatment status information, facilitates data aggregation and visualisation, and permits real-time feedback loops during implementation of mass drug administration. Here we describe the system’s design and use within the DeWorm3 Project and key features, and by sharing the register here, we hope our readers will further explore its use within their research and disease-control activities.

A prediction of dust mite populations in different categories of housing quality in Auckland, New Zealand

New Zealand has one of the highest asthma rates in the world, frequently linked to the quality of housing. A confirmed causal factor for asthma is the dust mite allergen, with environmental factors such as temperature and relative humidity (RH) linked to the proliferation of dust mites. Whilst significant research has been undertaken into the habitat and ecology of dust mites, little research has been conducted comparing different categories of housing quality in relation to dust mites. A dust mite population prediction algorithm (POPMITE) was used to undertake a comparative analysis of the potential of three housing types (old, newly constructed and newly constructed green certified) to harbour dust mites. The results determined no statistically significant difference between the quality categories, and the hypothesis that newly constructed, more insulated dwellings would have lower predicted dust mite populations was rejected. Instead, the newly constructed dwellings exhibited a wide range of predicted dust mite populations indicating a potential sensitivity to occupant behaviour in new housing stock which was not experienced in the older vintage dwellings. Occupant behaviour is suggested as an influencing factor with a call for policy makers to consider real-time feedback loops to dwelling occupants, in conjunction with structural interventions, to improve the health of the interior environment.

Welcome in the machine: Human–machine relations and knowledge capture

This article discusses new technologies in regard to their potential to capture workers’ situated knowledge. Machines are said to substitute but also to contribute to the labour process in collaboration with human skill sets. ‘Industry 4.0’ became the policy-wide shorthand to describe the new quality of real-time interconnectedness and feedback loops, known as cyber-physical systems within industry and engineering sciences. Data flows generated in these systems are used to continuously improve work processes by extracting information down to the very micro-level of neuroergonomics. In this process, workers’ interactions with the system are extracted, fed back and processed for future use and improvement. The article argues that in addition to the potential for extraction of new (bodily) knowledge, shifting skill use and the potential for new forms of control, new technologies contain the potential to extract situated knowledge owned by the worker and crucial for resistance and collective struggles.

Conceptualizing the “Corporate Nervous Net”: Decentralized Strategic Communication Based on a Digital Reporting Indicator Framework

ABSTRACTThe digital revolution challenges strategic communication. Artificial Intelligence (AI), Big Data, and the Internet of Things (IoT) create a rapidly changing environment for organizations as well as system complexities. To fulfill its task in ensuring the long-lasting success of organizations’ strategic communication needs to continuously adopt to this revolution. This article approaches the question of how strategic communication can adapt to the digitalization. In order to do so, the article conceptualizes the corporate nervous net and a predictive reporting indicator module with real-time feedback loops. As a result, the strategic communication body of knowledge is extended to include digital, assisted, decentralized communication. Decentralized strategic communication proposes a self-organizing, bottom-up approach of strategic communication under the principle of subsidiarity. It keeps complexity at a manageable level and enables the usage of local knowledge and quick adaptation to rapid changes. The proposed resilient approach to strategic communication uses the driving forces of the digital revolution of big data, AI and IoT in its favor instead of trying to control them.

Active Phase Stabilization for the Interferometer With 128 Actively Selectable Paths

A variable-delay optical interferometer with 128 actively selectable delays and a technique of active phase stabilization are innovatively designed and applied for the first time in the experiment of round-robin differential phase shift quantum key distribution (RRDPS-QKD). According to the RRDPS protocol, larger number of delay channels in interferometer can ensure higher tolerance of bit errors, eliminating the fundamental threshold of bit error rate of 11% of traditional BB84 protocol. Considering that the implementation of RRDPS protocol relys on the realizaiton of the interferometer, therefore, an interferometer with 128 selectable delay paths is constructed and demands the ability of fast switching at the rate of 10 kHz, which requires dynamic stability of multiple paths. Thus, a specific designed phase stabilization technique with closed real time feedback loop is introduced to guarantee the high visibility of interferometer selections dynamically. The active phase stabilization technique employs a phase modulator (PM) driven by a DAC to adjust the relative phase between the two arms of the interferometer. By monitoring photon counting rates of two Up-Conversion Detectors (UCD) at two output ports of the interferometer, a Field Programmable Gate Array (FPGA) calculates and finds the optimal code value for the DAC, maintaining a high visibility of the interferometer every time a new light path is selected. The visibility of most of the 128 interferometer selections can simultaneously be maintained over 96% during the QKD, which lays the foundation for the RRDPS experiment.

FPGA-based novel real-time evaluation and data quality monitoring system for tokamak high-performance GEM soft X-ray diagnostic

Based on literature review data quality monitoring topic is often being simplified in case of tokamak diagnostic systems. In previous publications Authors presented a novel model of real-time evaluation and data quality monitoring (EDQM) sub-system for use in physics experiments, mostly related to tokamak plasma measurements. The implementation of model is done in scope of monitoring raw output signals from Gas Electron Multiplier detectors used in FPGA-based soft X-ray diagnostics. System is designed to work in the real-time feedback loop for parameters control of tokamak in scope of achieving long-term plasma stability. In the paper are described results of application the DQM engine from measurements with 112-channel GEM detector and real radiation sources (isotopes, X-ray tube). The discussion provided in article covers the usefulness of applied novel EDQM techniques: technical and statistical, in scope of detection of new, corrupted output signals from the GEM detector when working under hard environment conditions. The presented in the paper results indicates the ability of the detection of subtle system defects (e.g. detector, electronics), resulting in the significant corruption of the output products of measured radiation: energy and topological spectra. This is especially important for the systems working in the real-time feedback loop. The novel approach of detection new malformed signals with ability to automatically discard those phenomena from further analysis is also described. In the summary there are described benefits of usage of the proposed novel DQM approach with indication of another unique work for further CPU extensions of the EDQM sub-system providing complex FPGA-CPU high-performance data quality monitoring unit. The EDQM system is planned to be installed in GEM SXR diagnostics on WEST tokamak.

Towards a Cooperative Architecture Platform

This paper outlines the use of videogame simulations as a representation of the complex interactions of resources within an urban neighborhood. The research advances the use of videogames as a mechanism for the production of design patterns for the city, in the hands of its inhabitants. By establishing a real-time feedback loop between players and an ecological simulation of the city, a user can learn and make decisions that could be shared with a community. This paper will mainly discuss the research developed through the videogame Block’hood, a project developed by the Author at the University of Southern California School of Architecture and the Plethora-Project research that attempts to critically address the use of mass media technology for collective organization and value production.