Prototype Of Framework Research Articles

Virtual prototyping for the textile industry: a framework supporting the production of crocheted objects

ABSTRACT Over the last years, many progresses have been made in the field of virtual prototyping, pushed by the interest of industries and artisans. Especially in the context of the textile industry, the digitizing of the prototyping stage offers the possibility to validate the product design choices before committing to the market. This paper presents a framework for the virtual prototyping of crocheted objects. The core of the framework is an algorithm that is capable of generating the crocheting patterns for a given object and the corresponding instructions. The instructions are leveraged by the framework to visualize the 3D geometry of the object, and can be also used to craft it. Compared to previous works, the proposed algorithm combines a number of features (primarily, the use of parametric surfaces and the support for short rows) that can reduce the distortions in crafted object shape while also lowering computational cost; the algorithm is also able to consider material- and style-related information. The results of a comparison between the proposed algorithm and state-of-the-art approaches showed improved performance in terms of similarity of the generated shape with the target one, computation time, and appearance of the crafted object.

A generic framework for glacial lake outburst flood investigation: A case study of Zalai Tsho, Southeast Tibet

Glacial lake outburst floods (GLOFs) are one of the most severe natural disasters, resulting in enormous socio-economic losses as well as threatening downstream settlements and infrastructure in the alpine region. Understanding historical GLOFs benefits disaster reduction and landscape conservation, but many GLOF events remain unreported or lack critical attributes. In this study, a novel framework was proposed to detect historical GLOFs and populate related attributes using a combination of Landsat, Sentinel-2 and Planet optical images, Sentinel-1 Synthetic Aperture Radar (SAR) images, field surveys, and documentation. The framework was implemented successfully on the case of Zalai Tsho, which revealed a previously unknown GLOF with critical attributes including the associated lake evolution, the flood process, and the impacts. The Zalai Tsho GLOF event in Bomi County, Southeast Tibet, occurred on July 7, 2018. Zalai Tsho emerged in 1994, and its surface area increased by 178% between 1994 and 2018, followed by a complete disappearance after the dam failure. A total water volume of 2.7 ± 0.2 × 105 m3 was released from Zalai Tsho to generate a peak discharge of 558.4 m3/s estimated by hydrological models, which destroyed a bridge, 23.2 ha of woodland, and 1.7 ha of farmland. The Zalai Tsho GLOF was initiated by intense melting waters, superimposed on water from an upstream supraglacial lake, eventually resulted in serious socio-economic losses and long-term environmental effects. Post-GLOF conservation projects play a positive role in protecting residents against flooding, while soil erosion has been exacerbated in certain areas without artificial countermeasures. This study provided an up-to-date, prototype framework for the identification of historical GLOF events to enrich the GLOF dataset and contributed to the risk assessment of GLOF and landscape conservation.

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition.

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.

A novel open-source cloud control platform with application to tracking control under disturbance

This paper presents a novel rapid control prototyping (RCP) framework for cloud control systems (CCSs). This new architecture consists of human–computer interaction (HCI) client, a cloud server, and an edge controller. It is designed based on open-source hardware and software, with the advantages of low cost, high flexibility, and easy replication. It can offer a reference design for better and further research of CCSs. Then, a cloud-based maglev control system (CMCS) is developed, and a case study on tracking control with disturbance is performed. Furthermore, a dual-structural controller based on equivalent-input-disturbance (EID) and networked predictive control (NPC) approach is devised to enhance the disturbance rejection performance for CCSs with communication constraints. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. A discrete-time EID (DEID) estimator is introduced for one subsystem to compensate for the time delay and the exogenous disturbance. For another subsystem, a time-event-driven strategy-based NPC method is devised to increase the dynamical performance of handling packet loss. Finally, experimental results verified the effectiveness and superiority of the proposed framework and the DEID-based dual-structural control algorithm.

Framework for Rapid Prototyping of PHM Analytics for Complex Systems using a Supervised Data-Driven Approach

Prognostic and Health Management (PHM) solutions are becoming increasingly popular in industries that rely on largesystems such as aircraft, spacecraft, and power plants. PHM analytic solutions are designed to monitor the health of eachsubsystem and component and apply predictive analytic to improve system reliability and safety, reduce the cost and decrease time spent on unscheduled maintenance. However, identifying correlations between different components andassociated monitors in these large systems can be challenging. To address this issue and achieve maximum utilization of available monitoring signals, a methodology is required that can identify correlations between degraded or failed components and the features engineered from the monitors and sensors. This paper introduces a framework that enables rapid prototyping of analytics, allowing users to seamlessly move from designing and discovering features to developing models for a specific event or component of interest. The framework has three main components: feature exploration, datapreparation, and model development. Feature exploration focuses on feature engineering using raw monitor data from all available monitors. Data preparation purges the data, and down-selects relevant features based on correlation defined in the feature exploration part. The data preparation step also creates a training dataset. Model development enablesquick testing and comparison of multiple supervised Machine Learning (ML) models. To demonstrate the framework, this paper presents an example of a remaining useful life model for an aircraft component. While the examples and simulations are aircraft-focused, the principles behind the framework can be applied to other large systems.

Cluster-MLP: An Active Learning Genetic Algorithm Framework for Accelerated Discovery of Global Minimum Configurations of Pure and Alloyed Nanoclusters.

Structural characterization of nanoclusters is one of the major challenges in nanocluster modeling owing to the multitude of possible configurations of arrangement of cluster atoms. The genetic algorithm (GA), a class of evolutionary algorithms based on the principles of natural evolution, is a commonly employed search method for locating the global minimum configuration of nanoclusters. Although a GA search at the DFT level is required for the accurate description of a potential energy surface to arrive at the correct global minimum configuration of nanoclusters, computationally expensive DFT evaluation of the significantly larger number of cluster geometries limits its practicability. Recently, machine learning potentials (MLP) that are learned from DFT calculations gained significant attention as computationally cheap alternative options that provide DFT level accuracy. As the accuracy of the MLP predictions is dependent on the quality and quantity of the training DFT data, active learning (AL) strategies have gained significant momentum to bypass the need of large and representative training data. In this application note, we present Cluster-MLP, an on-the-fly active learning genetic algorithm framework that employs the Flare++ machine learning potential (MLP) for accelerating the GA search for global minima of pure and alloyed nanoclusters. We have used a modified version the Birmingham parallel genetic algorithm (BPGA) for the nanocluster GA search which is then incorporated into distributed evolutionary algorithms in Python (DEAP), an evolutionary computational framework for fast prototyping or technical experiments. We have shown that the incorporation of the AL framework in the BPGA significantly reduced the computationally expensive DFT calculations. Moreover, we have shown that both the AL-GA and DFT-GA predict the same global minima for all the clusters we tested.

Construction management in space: explore solution space of optimal schedule and cost estimate

Multi-planetary life is one of humanity's audacious dreams. A key challenge towards achieving such a space mission is the design and construction of space habitats, for instance, on Mars. This paper presents a virtual space construction decision framework (SCDF) prototype developed and tested to address the question: "How can space construction project partners make informed decisions and leverage new construction methods and cutting-edge technologies that are developed and transform the AEC terrestrial industry?" We consider six practical and theoretical points of departure reflecting knowledge and technology and their application towards developing SCDF: General Contractor Workflow; BIM; Generative Scheduling and Construction Schedule Optimization; Construction Robotics; 3D Printing; Virtual Reality (VR) and Visualization. SCDF development applied virtual design and construction (VDC) to model - simulate - optimize - visualize - validate a space construction project by exploring the solution space in the context of extra-terrestrial construction environments from concept design to construction completion in the virtual environment before any mission is launched. Results confirm that insights from terrestrial construction apply to extra-terrestrial construction and vice versa. These insights contribute to the six points of departure at three levels: 1. The SCDF; 2. Extensions to existing technology platforms; 3. New approaches and methods.

New development: A prototype framework to assess the coverage of financial management topics in MPA/MPM programmes

IMPACT The development of a prototype framework to support analyses of Master’s of Public Administration (MPA) and Master’s of Public Management (MPM) programmes is expected to significantly impact the field of public administration and management. These programmes play a crucial role in equipping current and future public administrators with the essential skills needed to effectively navigate the complexities of the public sector. To date, the debate about public sector accounting education misses vital cross-country analysis to understand the main differences between countries.

Frappé: An Ultra Lightweight Mobile UI Framework for Rapid API-based Prototyping and Environmental Deployment

QR codes have been used as an inexpensive means to connect users to digital platforms such as websites and mobile applications. However, despite their ubiquity, QR codes are limited in purpose and can only redirect users to the URL contained within it, thereby making their use heavily network dependent which can be unsuitable for use in ephemeral scenarios and areas with limited connectivity. In this paper, we introduce Frappé, a framework capable of deploying ultra lightweight UIs to mobile devices directly through QR codes, without requiring any network connectivity. This is achieved by decomposing the UI into metadata and storing it inside the QR code, while offloading the UI functionality to API calls. We also introduce enFrappé, a WYSIWYG tool for building Frappé UIs. We demonstrate the lightweight nature of our framework through a technical evaluation, whereas the usability of our UI builder tool is demonstrated through a user study.

NeXT: Architecture, prototyping and measurement of a software-defined testing framework for integrated RF network simulation, experimentation and optimization

To support rigorous and repeatable experimental evaluation of wireless networked systems, the community has made significant efforts to develop experimentation platforms. However, existing platforms primarily focus on the data plane, i.e., the forwarding infrastructure, without explicitly considering the control plane. To fill this gap, in this work we develop NeXT, a software-defined playground with integrated wireless network simulation, experimentation and optimization capabilities. We first design the data plane, which integrates an event-driven broadband wireless network simulator called UBSim and a software-defined wireless network testing facility called RoboNet. We then design NeXT’s control plane, where a software toolchain is developed and deployed to support both traditional model-based optimization and new data-driven control techniques. We showcase the experimentation capability of NeXT considering a series of optimization and control problems in different wireless networks.

Consistency of p-norm based tests in high dimensions: Characterization, monotonicity, domination

Many commonly used test statistics are based on a norm measuring the evidence against the null hypothesis. To understand how the choice of that norm affects power properties of tests in high dimensions, we study the consistency sets of p-norm based tests in the prototypical framework of sequence models with unrestricted parameter spaces, the null hypothesis being that all observations have zero mean. The consistency set of a test is here defined as the set of all arrays of alternatives the test is consistent against as the dimension of the parameter space diverges. We characterize the consistency sets of p-norm based tests and find, in particular, that the consistency against an array of alternatives cannot be determined solely in terms of the p-norm of the alternative. Our characterization also reveals an unexpected monotonicity result: namely that the consistency set is strictly increasing in p∈(0,∞), such that tests based on higher p strictly dominate those based on lower p in terms of consistency. This monotonicity property allows us to construct novel tests that dominate, with respect to their consistency behavior, all p-norm based tests without sacrificing asymptotic size.

Continuous and Transparent E-Invigilation of E-Assessments

A massive number of users are currently utilizing e-learning. Despite the flexibility provided, the traditional methods of course delivery are still used, including e-examinations. There is significant concern about the potential for cheating. The current solutions in this respect fundamentally fail to offer the required level of security. This paper seeks to develop an e-invigilator that will provide continuous and transparent invigilation of examinees. The study involves a detailed presentation of the proposed architecture and a complete design to be the core of the system, which captures, processes, and monitors students in a controlled and convenient fashion. The proposed framework prototype was developed, presented, and utilized, involving 51 participants to conduct an experiment to explore the viability of the proposed framework. For all 51 participants in this experiment, the false rejection rate was 0 in 2D facial recognition mode, while in 3D facial recognition mode it was 0.04827. Moreover, in order to evaluate the robustness of the approach against targeted misuse, three participants were tasked with a series of nine threat scenarios. The false acceptance rate was 0.038 in the 2D mode and 0 in the 3D mode.

End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles.

Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. However, there are two major research gaps in existing works: (i) they lack fusion (and synchronization) of multi-sensors, camera, LiDAR and radar; and (ii) generic scalable, and user-friendly end-to-end implementation. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as camera, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors, namely, camera, LiDAR, and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications and suitable for quick and large-scale practical deployment.

A framework for precision “dosing” of mental healthcare services: algorithm development and clinical pilot

BackgroundOne in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients.MethodsHistorical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as “session dosing”: 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients.ResultsThe developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified.ConclusionsIt is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.

Jay: A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

AbstractWe present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Neural-logic multi-agent system for flood event detection

This paper shows the capabilities offered by an integrated neural-logic multi-agent system (MAS). Our case study encompasses logical agents and a deep learning (DL) component, to devise a system specialised in monitoring flood events for civil protection purposes. More precisely, we describe a prototypical framework consisting of a set of intelligent agents, which perform various tasks and communicate with each other to efficiently generate alerts during flood crisis events. Alerts are only delivered when at least two separates sources agree on an event on the same zone, i.e. aerial images and severe weather reports. Images are segmented by a neural network trained over eight classes of topographical entities. The resulting mask is analysed by a Logic Image Descriptor (LID) which then submit the perception to a logical agent.

AI Based Farmer’s Assistance Chatbot

Abstract: India has established itself as a prominent player in the global agriculture sector and has predominantly relied on an agriculture-based economy. However, a significant number of farmers and individuals involved in agriculture face various challenges due to limited knowledge of agricultural production. These challenges include inadequate farming techniques, insufficient crop materials, improper crop planning, and imbalanced fertilizer usage. This project presents a prototype of a chatbot framework designed to assist individuals, including farmers, in managing their crops and predicting their needs. The chatbot predicts fertilizer requirements, nutrient levels in crops, and provides valuable knowledge to users regarding basic agricultural needs. The structure employs Natural Language Processing and draws information from pre-determined data to deliver responses to user queries. To ensure accuracy, the chatbot analyzes previous interactions and sources data from "The Indian Council of Agricultural Research."

Simulation-Based Engineering of Heterogeneous Collaborative Systems—A Novel Conceptual Framework

We discuss the collaboration support of loosely coupled Smart Systems through configurable hyper-frameworks. Based on the system-of-systems (SoS) paradigm, in this article, we propose the model of a novel extendible conceptual framework with domain-specific moderation support for model-based simulations and the engineering of complex heterogeneous systems. The domain knowledge meta-model and corresponding management enterprise architecture enable the creation of template-based specializations. The proposed SoS conceptual framework meta-model represents an initial framework prototype that supports modeling, simulation, analysis, and utilization of dynamic architecting of heterogeneous SoS configurations. A Smart-Habitat concept encapsulating Smart-Area, Smart-City, Smart-Lot, Smart-Building, and Smart-Unit abstractions illustrate the frameworks’ applicability. The proposed SoS conceptual framework represents the initial conceptual support for modeling, simulation, analysis, and dynamic architecting of heterogeneous SoS configurations. We plan to refine the component architecture meta-model, specify a language workbench with Domain-Specific Orchestration Language support, and verify the configuration-based simulation manifest creation. These actions lead to the framework’s next stage, an operational framework (OF) instance, as a transitional artifact to the aimed software framework (SwF) counterpart.

CO-Creation and Evaluation of Food Environments to Advance Community Health (COACH)

Food environments are a key determinant of food intake and diet-related health. This paper describes the development of an iterative, adaptive, context-specific framework for health-enabling food environments embedded in cocreation theory. A 3-stage multimethod framework for the coproduction and prototyping of public health interventions was followed in an iterative manner during the development of the framework. These 3 stages were (1) evidence review, including systematic review, consultation with experts, and observation of current work; (2) codesign of the framework prototype with multiple stakeholders; and (3) coproduction through refinement of the prototype through stakeholder workshops and expert reviews with incorporation of researcher notes and workshop evaluation. We use the term prototype during the development phase and the term framework to report on the final product. COACH (CO-creation and evaluation of food environments to Advance Community Health) is a process framework that describes what best practice application of cocreation in health-enabling food retail environments should involve. COACH consists of 10 interdependent factors within a 4-phase continuous quality improvement cycle. The 4 phases of the cycle are engagement and governance establishment, communication and policy alignment, codesign and implementation, and monitoring and evaluation. Utilizing cocreation theory represents an innovative step in research and practice to improve the healthiness of food retail environments. COACH provides a specific, unique, and comprehensive guide to the utilization of cocreation to improve the healthiness of food environments in practice.

Cobot uptake in construction: embedding collaborative robots in digital construction processes

The paper at hand investigates the interrelation of automation and collaboration in digital construction processes. Labor shortage, demographic change, and a deficit in productivity motivate automation in construction. While the automation of single tasks is on its way, there is a lack of collaboration between automated equipment and robots along the digital construction process. To foster the development of collaborating robots, definitions and classification criteria for automation and collaboration activities are given. With these criteria at hand, it is possible to classify scientific examples from literature. On top of that, the paper introduces a prototyping framework for automated and collaborative equipment. The framework is thoroughly tested in an earthworks case study consisting of automated and collaborative excavation and compacting of an area. Through the collaboration of an automated excavator and vibratory plate, it is possible to simultaneously execute the ‘excavation’ and ‘compaction’ task, speeding up the overall earthworks process by a factor of almost two. Along with a higher productivity, the high degree of automation allows for safer work, as less workers are exposed to dangerous workspaces and the quality increases through continuous quality checking and integrated documentation of as-built data in BIM models.