Input Output Research Articles

Structural design and coupling analysis of multimode variable coupling parallel mobile robots

Purpose There is coupling between the branches of mobile parallel robots, similar to traditional parallel mechanisms, but there is currently relatively little research on the coupling problem between the branches of mobile parallel robots. Design/methodology/approach This study optimizes the coupling analysis method of traditional parallel mechanisms, treats the mobile parallel mechanism as a whole, takes the motion of the active pair as input and the overall motion of the mobile parallel mechanism as output and analyzes the input–output characteristics of the mobile parallel mechanism. Moreover, this study applies this theory to a mobile parallel mechanism, designs control logic and finally conducts simulation and physical verification. Findings This study proposes a coupling analysis method suitable for parallel mobile robots and designs the control logic of their active pair based on the results of their coupling analysis. This study designs a multimode variable coupling parallel mobile robot, which can change the coupling of the mechanism by changing its own branch chain structure, so that it can switch between different coupling configurations to meet the different needs brought by different terrains. Originality/value The work presented in this paper propose a method for analyzing the coupling of mobile parallel robots and simplify their control logic by applying coupling theory to the design of mobile parallel robots. This study conducts simulation and physical experiments, thereby filling the gap in the coupling analysis of parallel mobile robots and laying the foundation for the research of uncoupled parallel mobile robots.

Job creation and decarbonization synergies in Latin America: a simulation-based exploratory modeling analysis

This study employs an Input–Output simulation model to assess the potential employment effects resulting from investments in 40 sector-specific decarbonization transformations across Latin America and the Caribbean. Using a Multi-region Input–Output framework (Eora26), our findings indicate that investments in energy production and buildings sectors offer promising job creation opportunities, averaging 5.5 total jobs per $1 million invested. Similarly, in the waste, industry and agriculture, forestry, and land use sectors demonstrate significant potential, yielding approximately 5 total jobs per $1 million. The analysis models investments as demand vectors, producing results for 17 countries in the region. These estimates endogenize the diverse economic structure and state of development of these countries. We argue that country-level analysis is needed to identify climate strategies that maximize job creation while achieving net-zero emissions.

Optimizing properties of clayey soil using lime and waste marble powder: a sustainable approach for engineering applications

Several studies have explored the potential of waste marble powder (WMP) and lime (LM) as solutions for issues associated with clayey soils. While WMP enhances mechanical properties and addresses environmental concerns, LM effectively improves soil characteristics. This research investigates the efficacy of LM and WMP, both individually and in combination, in addressing challenges specific to clayey soils in Bouzaroura El Bouni, Algeria. These soils typically exhibit low load-bearing capacity, poor permeability, and erosion susceptibility. LM demonstrates promise in enhancing soil properties, while WMP not only addresses environmental concerns but also enhances mechanical characteristics, providing a dual benefit. The study utilizes a three-variable experiment employing Response Surface Methodology (RSM) Box-Behnken Design, with variations in clay content (88%–100%), LM treatment (1.5%–9%), and WMP inclusion (1.5%–9%). Statistical analysis, including ANOVA, reveals significant patterns with p-values <5%. Functional relationships between input variables (clay, LM, and WMP) and output variables (cohesion, friction angle, and unconfined compressive strength) are expressed through high determination coefficients (R2 = 99.84%, 77.83%, and 96.78%, respectively). Numerical optimization identifies optimal mixtures with desirability close to one (0.899–0.908), indicating successful achievement of the objective with 88% clay content, 3% LM, and 6% WMP. This study provides valuable insights into optimizing clay soil behavior for environmental sustainability and engineering applications, emphasizing the potential of LM and WMP as strategic additives.

Decomposition of China’s Carbon Emissions Responsibility from the Perspective of Technological Heterogeneity

A global agreement has been reached on the reduction in greenhouse gas emissions. Worldwide, countries have implemented measures to tackle carbon emission issues by establishing aggregate targets and decomposing responsibilities. This study aims to decompose carbon emissions by creating an input–output model that incorporates multivariate factors like energy consumption and water consumption, together with a ZSG-DEA (zero-sum data envelopment analysis) model considering technological heterogeneity (Tech-ZSG-DEA). Based on the total carbon emission data predicted using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the carbon emission efficiency of 30 provinces in China is evaluated according to multiple principles. This is achieved by considering variations in population, economy, energy consumption, and water consumption across different locations. The efficiency findings indicate a discrepancy between the initial allocation and the highest efficiency value of 1. The traditional ZSG-DEA model overlooks regional disparities and may worsen carbon emission pressures in less developed areas. In contrast, the Tech-ZSG-DEA model, which considers regional technological diversity, allows more efficient regions to help alleviate some of the carbon emission burden and considers economic and social benefits. There is a large difference in the emission responsibility of the provinces based on the different decomposition principles. Finally, relevant policy recommendations are provided, such as the formulation of differentiated and inclusively coordinated emission plans. In addition, there are also mechanisms for coordinating interests and joint prevention among different regions.

Multi-Output Physically Analyzed Neural Network for the Prediction of Tire–Road Interaction Forces

<div>This article introduces an innovative method for predicting tire–road interaction forces by exclusively utilizing longitudinal and lateral acceleration measurements. Given that sensors directly measuring these forces are either expensive or challenging to implement in a vehicle, this approach fills a crucial gap by leveraging readily available sensor data. Through the application of a multi-output neural network architecture, the study focuses on simultaneously predicting the longitudinal, lateral, and vertical interaction forces exerted by the rear wheels, specifically those involved in traction. Experimental validation demonstrates the efficacy of the methodology in accurately forecasting tire–road interaction forces. Additionally, a thorough analysis of the input–output relationships elucidates the intricate dynamics characterizing tire–road interactions. This research underscores the potential of neural network models to enhance predictive capabilities in vehicle dynamics, offering insights that are valuable for various applications in automotive engineering and control systems.</div>

A Local Approach to Parameter Space Reduction for Regression and Classification Tasks

Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and surrogate models’ design, especially when in presence of high-dimensional parametrized systems. In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter space. The clustering is performed without losing the input–output relations by introducing a distance metric induced by the global active subspace. We present two possible clustering algorithms: K-medoids and a hierarchical top–down approach, which is able to impose a variety of subdivision criteria specifically tailored for parameter space reduction tasks. This method is particularly useful for the community working on surrogate modelling. Frequently, the parameter space presents subdomains where the objective function of interest varies less on average along different directions. So, it could be approximated more accurately if restricted to those subdomains and studied separately. We tested the new method over several numerical experiments of increasing complexity, we show how to deal with vectorial outputs, and how to classify the different regions with respect to the LAS dimension. Employing this classification technique as a preprocessing step in the parameter space, or output space in case of vectorial outputs, brings remarkable results for the purpose of surrogate modelling.

Variable Switching Frequency for ZVS over Wide Voltage Range in Dual Active Bridge

The Dual Active Bridge (DAB) converter is known for its advantageous characteristics, including bidirectionality, galvanic isolation, and soft-switching operation. However, achieving Zero Voltage Switching (ZVS) across the complete operation range is not guaranteed, particularly through a wide input–output voltage ratio. This paper explores the integration of the switching frequency as a control variable in the DAB converter to ensure ZVS operation across a wide voltage range, employing Single Phase Shift (SPS) modulation. The study evaluated the RMS and reactive currents under variable switching frequency, presenting the advantages of this approach. Moreover, it includes a design-oriented analysis of the ZVS limits and their relationship with the switching frequency, aiming to ensure ZVS at any operating point. Experimental results validated the theoretical analysis, while presenting the main advantages of variable switching frequency implementation.

Multidirectional cascade of square MIMO LTI subsystems: Transfer function representation

This paper considers continuous transfer function representation for a cascade composed of N mutually interconnected square multiple-input multiple-output (MIMO) linear time-invariant (LTI) subsystems with general multidirectional input–output configurations. Such cascaded structure can arise, e.g., from the spatial discretization of one-dimensional distributed parameter systems (DPSs) described by partial differential equations (PDEs) with boundary conditions representing different configurations of boundary input signals. As shown in the paper, the transfer function matrix of the resulting cascade can be calculated using the Redheffer star product of the subsystems’ transfer function matrices, which simplifies into the usual matrix product for the unidirectional cascade. For the particular case of 2 × 2 cascade composed of rational transfer function subsystems, the recursive formulas for its boundary and distributed transfer functions have been derived for both uni- and multidirectional configurations. The well-posedness and the stability criteria are also analyzed, proving to be more restrictive for the multidirectional cascade than for the unidirectional one due to the internal positive feedback in the first case. As shown in the attached example, the presented results can be used to approximate one-dimensional distributed parameter systems using rational transfer function subsystems representing the spatial sections of the original DPS.

Carbon Footprint Reduction by Reclaiming Condensed Water

Everyday activity incurs carbon footprints, which are classified as personal, production, organizational and national, and may be assessed by input–output analysis (IOA), life-cycle assessment (LCA), or the combination of LCA and IOA methods. Notwithstanding international standards, like ISO 14064 and Publicly Available Specification (PAS) released for standardization, carbon footprint results can vary and sometimes lack consistency that due to variations in data sources, crossover boundary definitions, and operational boundaries for indirect emissions. The novelty of this study is the direct utilization of condensed water in an existing cooling water system, without the need for prior wastewater treatment, as typically required for greywater. The lack of practical case studies exploring the water–energy nexus in the context of reclaiming condensed water for evaporative cooling tower systems makes this research particularly significant. This highlights that condensed water can be a straightforward and cost-effective solution for both water conservation and energy savings. This case study highlights the benefits of reclaiming condensed water as supplementary cooling water, which proved effective in water quality treatment and dilution augmentation, considering that a higher cycle of concentration (CoC) was achieved, leading to reduced bleed-off that resulted in a water saving of 44% for make-up and 80% for bleed-off water, and energy savings from 6.9% to 13.1% per degree Celsius of condensing refrigerant temperature (CRT). The analytical assessment revealed that reclaiming condensed water is a promising answer for green building and is a by-product of condensation without extra power demands, avoiding the generation of an increased carbon footprint and exacerbation of greenhouse gas (GHG) emissions from freshwater resource extraction, and for the production of energy-efficient devices or substitutions. By eliminating the need for wastewater treatment, this research enhances the practicality and feasibility of direct use of condensed water in various applications. This approach not only promotes sustainability by conserving water and energy but also renews interest among proponents of green building practices. It has the potential to accelerate the adoption of this method and integrate it into green building designs.

Dual threshold input receiver FPGA-only signal digitization method for time-of-flight positron emission tomography

AbstractAs silicon photomultiplier (SiPM)-based time-of-flight (TOF) positron emission tomography (PET) becomes popular, the need for sophisticated PET data acquisition (DAQ) systems is increasing. One promising solution to this challenge is the adoption of a field-programmable gate array (FPGA)-only signal digitization method. In this paper, we propose a new approach to efficiently implement an FPGA-only digitizer. We configured the input/output (IO) port of the FPGA to function as a dual-threshold voltage comparator through the use of simple passive circuitry and heterogeneous IO standards. This configuration overcomes the limitations of existing methods by allowing different threshold voltages for adjacent IO pins, effectively reducing routing complexity and lowering manufacturing costs. An FPGA-only digitizer was implemented by integrating the dual-threshold voltage comparator and FPGA-based time-to-digital converter. By combining the dual-threshold time-over-threshold (TOT) method and curve fitting, precise energy information could be obtained. The performance of the FPGA-only digitizer was assessed using a detector setup comprising a 3 × 3 × 20 mm3 LYSO scintillation crystal and a single pixel SiPM. Using the configured evaluation setup, an energy resolution of 12.5% and a time resolution of 146 ± 9 ps were achieved for a 20 mm scintillation crystal. The dual-threshold TOT implemented using the proposed method showed consistent linearity across an energy range of 100 keV to 600 keV. The proposed method is well-suited for the development of cost-effective DAQ systems in highly integrated TOF PET systems.

SURVEY ON STABLE DIFFUSION TEXT TO IMAGE USING AI

The Fusion Nexus Text-to-Image Synthesis Initiative integrates cutting-edge Generative Adversarial Networks (GANs) with Natural Language Processing (NLP) techniques to narrow the semantic divide between textual input and visual output. Built upon the robust Stable Diffusion training paradigm, this initiative is engineered to produce immersive, true-to-life images based on descriptive text prompts. While GANs have exhibited potential in image generation, issues like mode collapse and training instability have impeded their effectiveness. The Fusion Nexus initiative circumvents these challenges by leveraging the Stable Diffusion framework, which furnishes a stable and reliable training methodology for GANs. By amalgamating recent advancements in deep learning, this project spearheads a novel approach to text-to-image synthesis. Its primary aim is to craft cohesive and highly realistic visual representations from textual descriptions, thereby bridging the gap between linguistic expression and visual perception. This ambitious undertaking marks a significant stride at the convergence of GANs and NLP, presenting a promising solution to the intricate task of text-to-image generation. Keywords: Adversarial, Diffusion, Framework, Fusion, GANs, Generation, Generation, Image, Language, Natural, NLP, Processing, Robust, Stable, Text-to-image, Training, Visuals.

AI Within Online Discussions: Rational, Civil, Privileged?

While early optimists have seen online discussions as potential spaces for deliberation, the reality of many online spaces is characterized by incivility and irrationality. Increasingly, AI tools are considered as a solution to foster deliberative discourse. Against the backdrop of previous research, we show that AI tools for online discussions heavily focus on the deliberative norms of rationality and civility. In the operationalization of those norms for AI tools, the complex deliberative dimensions are simplified, and the focus lies on the detection of argumentative structures in argument mining or verbal markers of supposedly uncivil comments. If the fairness of such tools is considered, the focus lies on data bias and an input–output frame of the problem. We argue that looking beyond bias and analyzing such applications through a sociotechnical frame reveals how they interact with social hierarchies and inequalities, reproducing patterns of exclusion. The current focus on verbal markers of incivility and argument mining risks excluding minority voices and privileges those who have more access to education. Finally, we present a normative argument why examining AI tools for online discourses through a sociotechnical frame is ethically preferable, as ignoring the predicable negative effects we describe would present a form of objectionable indifference.

Developing Functional Thinking: from Concrete to Abstract Through an Embodied Design

AbstractIn addressing the challenge of fostering functional thinking (FT) in secondary school students, our research centered on the question of how an embodied design can enhance FT’s different aspects, including input–output, covariation, and correspondence views. Drawing from embodied cognition theory and focusing on an action- and perception-based task design that uses light ray contexts and different function representations, we developed a digital-embodied learning environment, using the nomogram as a central representation. Our pilot study involving four eighth-grade students provided insights into their physical interactions with these modules through a multi-touch digital interface. Analysis of video and audio recordings from the pilots, including students’ hand gestures and verbal expressions, was guided by comparing hypothetical learning activities with the actual learning activities. The results show that (1) a concrete light ray context enables students to ground the abstract mathematical function concept; (2) the bimanual coordinating motion tasks, incorporating the covariation aspect of FT, allow students to connect their bodily experience with function properties; and (3) our embodied and dragging tasks support insight in the conversion between nomograms and graphs of functions, encouraging students’ correspondence thinking by providing multiple perspectives to understand, reason about, and manipulate the function. In conclusion, our findings suggest the potential of digital-embodied tasks in fostering FT, evident in students’ diverse strategies and reasoning.

On-farm energy input-output and economic analysis of important essential oil-bearing Mint species in subtropical India

Mint species are grown worldwide and known for their medicinal and aromatic properties however, studies on its energy assessment are very scarce. This study quantifies the energy input–output relationship of herbs and essential oil production of different mint species (M. arvensis, M. piperita, M. spicata, M. cardiaca and M. citrata) which were commercially grown in subtropical India. This study proposes for life cycle assessment and their effects on environment. The energy inputs and outputs were significantly varied with mints species. Results showed that mints essential oil production used more energy than herbs, and M. citrata utilizes the highest energy in both cases. Fire wood requirements for essential oil extraction accounts maximum (59.16 %) energy input. Mint herbs and essential oil yields were recorded higher in M. citrata (23.14 t ha−1), and M. arvensis (143.2 kg ha−1), respectively. Energy yields were estimated highest in M. cardiaca (herbs), and M. arvensis (essential oils), and required net calorific values were tested by automated advanced oxygen bomb calorimeter. Overall, M. arvensis showed the best results in energy production (0.07 kg MJ−1), profitability (0.27), net return (17156.40 MJ ha−1), and energy use efficacy (1.27) for mint herbs production. However, for essential oil production M. cardiaca performed well. This study revealed that mint herbs production was more energy efficient than essential oils extraction although essential oil production is fiscally secure.

Multimode Emission in GaN Microdisk Lasers

AbstractQuantum well nanolasers usually show single‐mode lasing, as gain saturation suppresses emissions in other modes. In contrast, for whispering gallery mode microdisk lasers with GaN quantum wells as active material, above threshold multimode laser emission is observed. This intriguing emission feature is manifested in the fact that several modes simultaneously show the characteristic kink in the input–output curve at the onset of lasing. A quantum theory for nanolasers is used to support the experimental finding and to analyze this behavior in the presence of gain saturation. Coupling effects between neighboring modes are identified as the origin of multimode lasing, which initiate photon exchange between modes via population pulsations similar to classical wave‐mixing effects. A reduction of this type of mode coupling with increasing mode spacing is demonstrated. The results can pave the way for multimode application of nanolasers in integrated photonic circuits.

Adaptive PMSM Control of Ship Electric Propulsion with Energy-Saving Features

Electric ship propulsion is considered one of the most promising alternatives to conventional combustion systems. Its goal is to reduce the carbon footprint and increase a ship’s maneuverability, operational safety, and reliability. The high requirements for ship propulsion make permanent magnet synchronous motors (PMSMs) an attractive solution due to their characteristics. This paper discusses the control problem of a PMSM based on the input–output feedback linearization method combined with the optimal and adaptive control techniques. The method presented here integrates the parameter tuning process with the optimal design of the baseline controller. Since the load disturbances are treated as an additional unknown parameter, there is no need to introduce an integral action to deal with the resulting steady-state error. An important feature of the designed controller is the so-called energetic optimization of the system; i.e., in addition to the aforementioned adaptive and optimal controller, it has a feature of ensuring zero reactive power consumed by the system. The performed simulations of the machine speed stabilization process confirmed the high efficiency of the proposed controller despite the assumed uncertainty of the system parameters and environmental (load) disturbances. Besides achieving high-quality control, an essential feature of this approach is the elimination of the tuning problem.

Economic effects of the hydrogen fuel cell sector in South Korea: An input-output analysis

The South Korean government plans to deploy approximately 200–300 MW of hydrogen fuel cells (FCs) annually from 2023 to 2036, all of which will be connected to the national power system, not used for self-consumption. The government is requesting quantitative information on the economic effects of the FC sector. In response to this request, this study delves into the four economic effects of expanding FC use in South Korea adopting the input-output (IO) approach for the first time in the literature. More specifically, it investigates how much investment or production in the FC sector affects other sectors. Economic performance, such as production, value-added, jobs, and wages, is the subject of investigation. The FC sector is dealt with separately as the FC-manufacturing (FC-M) sector and the FC-equipping and operating (FC-E&O) sector. The 2019 IO table, 34-sector classification, demand-driven model of the IO approach, and exogenization of the FC sector are used. The results show that each unit of investment or production in the FC-M and FC-E&O sectors triggers 2.328 and 1.793 units of production, 0.785 and 0.967 units of value-added, and 0.346 and 0.575 units of wages, for each sector, respectively, across the economy. KRW 1 billion of production or investment in the two sectors creates approximately 7.7 and 3.8 jobs, respectively. These figures can be useful for exploring the economic effects of FC deployment in the future on the South Korean economy.

Explainable artificial intelligence framework for FRP composites design

Fiber-reinforced polymer (FRP) materials are integral to various industries, from automotive and aerospace to infrastructure and construction. While FRP composite design guidelines have been established, the process of obtaining the desired strength of an FRP composite demands considerable time and resources. Despite recent advancements in Machine Learning (ML) models which are commonly used as predictive models, the inherent 'black box' nature of those models poses challenges in understanding the relationship between input design parameters and output strength of the composite. Moreover, these models do not provide tools to facilitate the designing process of the composite. The current study introduces an explainable Artificial Intelligence (XAI) framework that will provide understanding for the input–output relationships of the model through SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs). In addition, the framework provides for the first time a designing approach for adjusting the important design parameters to obtain the desired composite strength by the designer through utilizing an explainability technique called Counterfactual (CF). The framework is evaluated through the design of a 14-ply composite, successfully identifying critical design parameters, and specifying necessary adjustments to meet strength requirements.

Adaptive soft sensor modeling of chemical processes based on an improved just‐in‐time learning and random mapping partial least squares

AbstractThe just‐in‐time learning‐based partial least squares (JIT‐PLS) has been extensively applied to adaptive soft sensor modeling of complex nonlinear processes. However, it still has the problems of unreasonable relevant samples selection and unsatisfactory local modeling. Aiming at these problems, this paper proposes an improved just‐in‐time learning‐based random mapping partial least squares (IJIT‐RMPLS), including an improved relevant samples selection strategy and a random mapping PLS (RMPLS) model. On the one hand, considering the different correlation degrees between input variables and output variable, this method applies mutual information to evaluate the importance of each input variable and designs a variable‐weighted Euclidean distance to select relevant samples for local modeling. On the other hand, in order to prompt the prediction precision of local soft sensor models, this method combines the idea of nonlinear random mapping in extreme learning machines with PLS and builds a RMPLS with multiple activation functions. Applications on a numerical example and a real chemical process show that the proposed IJIT‐RMPLS has smaller prediction error compared with traditional JIT‐PLS.

Compact active analog device for novel applications useful for sensing and measurement

The presented research introduces a novel approach to modern modular active device and provides various practical application examples tailored for industrial sensing readouts. Straightforward implementation of single active device-based topologies in frequency bands from kHz up to MHz is presented. These applications include: an electronically linearly tunable special band-pass filter that can be easily modified into a voltage-controlled oscillator, a two-port quadratic transformer, an amplitude modulator, and an equalization circuit (which serves as a fractional-order differentiator and integrator). These circuits benefit from high-impedance voltage input and low-impedance output (easily matched to 50 Ω), high processed signal levels (often reaching hundreds of mV), and simplified topologies without redundant passive elements. These features are especially valuable in tunable and configurable solutions for modern communication, sensing, audio, and consumer electronic systems, as well as in modeling various physical and biological systems. The experiments have been provided by simulations and measurements using device VCA824.