Performance In Dynamic Environments Research Articles

Optimal charging scheduling for Indoor Autonomous Vehicles in manufacturing operations

Indoor Autonomous Vehicles (IAVs) have become instrumental in modern logistics, particularly in dynamic operational environments. Their consistent availability is crucial for both timely task execution and energy efficiency in smart factories. Therefore, an inefficient charging schedule can waste energy, compromising production and warehousing efficiency. In addition, formulating an effective charging schedule is challenging due to the nonlinearity of its design and the uncertainties inherent in smart factory settings. In contrast to previous studies that either simplify the problem using linearization-based methods or approach it with computationally demanding algorithms, this paper efficiently addresses the nonlinear challenge, ensuring a closer alignment with real-world conditions. Hence, a simulation environment is first designed to replicate a smart factory, including validated models of IAVs, Charging Stations (CSs), and a variety of unpredictable static and dynamic obstacles. A comprehensive dataset is then provided from this simulation setup. Utilizing this dataset, a model tailored to ascertain the travel time of IAVs, accounting for inherent uncertainties, is trained using Deep Neural Networks (DNNs). Subsequently, a Mixed-Integer Nonlinear Programming (MINLP) problem is formulated to design the optimization task. Finally, integration of the DNNs’ model with the Branch-and-Bound (BnB) approach, forming the BnBD method, streamlines the determination of the optimal charging schedule. Experimental results highlight significant improvements in charging scheduling, establishing this approach as a viable and promising solution for manufacturing operations.

The seasonal-trend disentangle based prognostic framework for PEM fuel cells

Prognostics and Health Management (PHM) offer a potential solution to improve the durability of Proton Exchange Membrane Fuel Cells (PEMFCs), where degradation prognostic is the key component of the PHM system. However, limited by the dynamic operating environment, accurate and efficient prognostic algorithms still required for real applications. In this paper, the Seasonal-Trend Disentangle (STD) based prognostic framework is proposed to enhance the Remaining Useful Life (RUL) prediction of PEMFC system. Specifically, a lightweight parameter estimation neural network is introduced to estimate the aging parameters of the FC degradation model directly from PEMFC measurements. Besides, the integral of the FC degradation model at the specified current density range serves as the comprehensive Health Index (HI), which could combine different aging parameters and mitigate the effect of load dynamics. The STD-based prognostic model effectively captures the time-invariant features in the HI sequence from trend and seasonal phases, improving Remaining Useful Life (RUL) prediction performance. The proposed prognostic model is compared with RNN-family, ESN, and Dilated CNN methods using three distinct durability datasets from FCs operating under static, quasi-dynamic, and cyclically varying dynamic current loads. Experimental results demonstrate that the proposed algorithm outperforms the reference methods in terms of RMSE and MAPE metrics in the aging prognostic, and the proposed method could obtain satisfactory RUL prediction results 100–150 h earlier than the reference methods.

Soldier Health and Position Tracking System

The Soldier Health and Position Tracking System (SHRYS) is an integrated technological solution designed to address the multifaceted challenges associated with monitoring and ensuring the well-being of military personnel in dynamic and unpredictable operational environments. This system amalgamates state-of-the-art wearable devices, GPS technology, and health monitoring sensors to provide comprehensive real-time insights into soldiers' physical conditions and locations. The wearable devices continuously collect vital health metrics such as heart rate, body temperature, and activity levels, fostering a proactive approach to healthcare management. Simultaneously, the GPS technology enables precise and continuous tracking of soldiers' positions on the battlefield. The collected data is then transmitted to a centralized monitoring system, where advanced analytics and machine learning algorithms assess soldiers' health trends, detect anomalies, and facilitate timely medical interventions. The SHPTS aims to enhance the overall situational awareness of military commanders, optimize resource allocation, and ultimately ensure the optimal health and safety of soldiers deployed in challenging and high-risk scenarios. This innovative system stands at the forefront of leveraging technology to safeguard the well-being and operational effectiveness of military personnel.

ЕКОСИСТЕМНИЙ ПІДХІД В ПРОЦЕСІ ФОРМУВАННЯ ІННОВАЦІЙНИХ БІЗНЕС-МОДЕЛЕЙ

The article systematizes theoretical approaches to the concept of “ecosystem”, including business ecosystem, economic ecosystem, innovation ecosystem, entrepreneurial ecosystem, industrial ecosystem. The article also considers the prerequisites and retrospective evolution of the transition to the work format according to the principle of ecosystems when companies build their business models. The authors proposed the definition of the ecosystem of innovative development of the industrial sector as a set of interacting business entities (real sector, business), authorities, public organizations, scientific and educational organizations, which realize the implementation of joint functional tasks based on the joint use of the resource potential of all participants (actors) in order to achieve a synergistic effect. The component composition of the ecosystem of innovative development of enterprises is proposed. Differentiation of the benefits of the participants from the application of the ecosystem approach (economic, social, institutional, infrastructural) is provided. The concept of the innovation ecosystem was studied for the analysis of dynamic open environments, which include organizations, individuals and institutions that interact in the formation, use and dissemination of innovations. The key components of innovative ecosystems are defined and their interaction is highlighted. The importance of using information and digital platforms to optimize business processes in the ecosystem is emphasized. The characteristics that are typical for innovation ecosystems and modern innovation processes are highlighted. An algorithm for the introduction of an ecosystem approach to the model of innovative development of the industrial sector has been developed. Significant attention is paid to defining the differences between digital ecosystems and traditional business alliances. The risks arising during the transition of work to this business format, as well as prospects, are considered in detail development and society’s readiness to work in ecosystems on the basis of accumulated customer service experience.

Adaptive Substation Infrastructure for Bangladesh: Harnessing Artificial Intelligence and Machine Learning for Enhanced Performance and Grid Resilience

This groundbreaking research endeavors to revolutionize substation engineering in Bangladesh by introducing an innovative paradigm that integrates artificial intelligence (AI) and machine learning (ML) methodologies. In response to the dynamic and challenging operational environment, this study focuses on the development of an adaptive substation infrastructure capable of intelligently responding to fluctuating energy demands, environmental stresses, and emerging grid complexities. Through the application of advanced AI algorithms, the research addresses real-time fault detection, predictive maintenance, and comprehensive condition monitoring within the substation framework. Harnessing the capabilities of ML models, the proposed infrastructure aims to optimize energy flow, enhance grid resilience, and mitigate potential failures by autonomously adapting to evolving operational scenarios. By combining cutting-edge technology with the unique challenges of the Bangladeshi power landscape, this research not only aims to advance the field of substation engineering but also holds the promise of significantly contributing to the sustainable development of the nation's power infrastructure. The findings are anticipated to guide the design and implementation of intelligent substation systems, ushering in a new era of efficiency, reliability, and adaptability in the Bangladesh power grid.

Distributed Navigation in Emergency Scenarios: A Case Study on Post-Avalanche Search and Rescue Using Drones

Unmanned aerial vehicles (UAVs) are increasingly employed in civil applications due to their ease of use and adaptability. This paper proposes a distributed navigation strategy for a formation of UAVs in post-avalanche search-and-rescue (SAR) operations. Formations offer a more efficient approach than single UAVs in dynamic and complex operational environments. Additionally, they can distribute different sensors, reducing payload and increasing robustness and overall efficiency. The proposed navigation algorithm relies on the Kalman filter (KF) based on consensus to distribute state estimation, and internodal transformation theory to improve system scalability, preserving the dynamic equivalence between the global and local models. The effectiveness of this approach was tested in two realistic scenarios, resulting in the ability to detect victims and maintain situational awareness while avoiding unsearched areas. The proposed approach offers a promising alternative to human-intensive SAR missions.

Flexible operation of Post-combustion CO2 capture process enabled by NARX-MPC using neural network

Post-combustion CO2 capture (PCC) process is expected to serve as the role of a bridge solution during the transition from fossil-based to renewable energy, but extensive energy consumption is a significant hurdle to its widespread commercial adoption. Operating a PCC process economically often requires large operating condition changes in response to power plant load changes and time-varying energy prices. The ‘flexible’ operation requires a controller that aids in such a dynamic operating environment of the PCC process. To balance model complexity with prediction accuracy, we adopt NARX-MPC which employs a neural network-based NARX model as a prediction model for MPC. Under various dynamic operation scenarios, the proposed NARX-MPC is evaluated and compared with a linear MPC (LMPC) controller. Our observations indeed reveal that the NARX-MPC delivers significantly superior closed-loop control performance over the LMPC, especially in scenarios involving large changes in the power plant load or inlet flue gas flow rate and simultaneous changes in the flue gas flow rate and the CO2 capture rate.

How auditors identify and report key audit matters - An organizational routines perspective

Despite the volume of auditing research over the last decade, relatively little is known about how auditors apply specific codified requirements. We use organisational routines as a schematic and Power's (2003) distinction between the front and backstage of an audit to explore the link between the broad prescriptions in ISA 701 and the micro-level performances of individual auditors to report key audit matters (KAMs). Interviews with 42 senior staff from large and mid-tier audit firms reveal how the ostensive elements of auditing routines result in coercive, normative and mimetic forces. These forces regulate and coordinate how audit procedures are executed to yield a frontstage representation of technical and procedural rigour. Yet, at the performative level experience, professional judgement and dynamic operating environments counter isomorphic forces. The messier backstage of activities provides an opportunity for minor modifications to audit procedures which can accumulate and impact the routine at the ostensive level. That the responsibility for the reporting KAMs vests with engagement leaders increases the opportunity for variability as they exercise professional judgement to modify the routine. Overall, our paper adds to the literature on how auditing is socially constructed and reveals how technical concepts are “transmitted” among individual practitioners to illuminate an understudied aspect of auditing. The study provides an original action-orientated perspective of how routines work in professional settings. It complements the largely inferential work on KAM determinants and should be relevant for standard-setters conducting a post-implementation review of ISA 701 and regulators monitoring KAM reporting.

Human-Flow-Aware Long-Term Mobile Robot Task Planning Based on Hierarchical Reinforcement Learning

The difficulty in finding long-term planning policies for a mobile robot increases when operating in crowded and dynamic environments. State-of-the-art approaches do not consider cues of human-robot-shared dynamic environments. Aiming to fill this gap, we present a novel Human-Flow-Aware Guided Hierarchical Dyna-Q (HA-GHDQ) algorithm, which solves long-term robot task planning problems by using human motion patterns encoded in Maps of Dynamics (MoDs). To tackle the complexity of long-term robot operation in dynamic environments, we propose a combination of symbolic planning and Hierarchical Reinforcement Learning (HRL) that generates robot policies considering cost information derived from MoDs. We evaluated HA-GHDQ in a factory environment with two simulation and one real-world datasets to complete a transportation-and-assembly task. Our approach outperforms the baselines with respect to sample efficiency and final plan quality. Moreover, we show that it is more adaptable and robust against environmental changes than the baselines.

Using HOQ Methodology to Prioritise Organisational Resilience Decisions in Training Establishments

AbstractResilience has become an attractive proposition in many organisations seeking to perform well under adversity and uncertainty in a dynamic and changing operational environment. We developed a survey tool to measure organisational resilience and implemented it in several large training establishments. The gap between the training organisations' actual and desired resilience was identified through analysis of the survey's quantitative responses. The survey's qualitative (free‐form) responses underwent inductive (thematic) and deductive (resilience attribute allocation) analysis to expand our understanding of training establishment problems and possible solutions from the survey participants' perspectives. Here we applied the House of Quality (HOQ) approach to prioritise the solutions to the organisational issues identified from the qualitative responses to align those with the organisational resilience attributes' priorities. The organisational resilience attributes were prioritised in another round of HOQ application to align them with the importance scores of the organisation‐specific resilience requirements. We used the results from our two HOQ rounds to create an optimal path (response) to move from actual to desired organisational resilience, demonstrated in a survey‐based resilience case study conducted in a large Defence training establishment.

Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments.

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic remapping, updating the map is recommended. However, in the case of incorrect robot pose estimation, updating the map can lead to errors that prevent the robot's localisation and jeopardise map accuracy. In this paper, we propose a safe Lidar-based occupancy grid map-updating algorithm for dynamic environments, taking into account uncertainties in the estimation of the robot's pose. The proposed approach allows for robust long-term operations, as it can recover the robot's pose, even when it gets lost, to continue the map update process, providing a coherent map. Moreover, the approach is also robust to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.

Value of Algorithm-Enabled Process Innovation: The Case of Sepsis

Problem definition: Algorithm-enabled decision support has an increasingly important role in supporting the day-to-day operations of healthcare organizations. Yet, fully realizing the value of algorithmic decision support lies critically in the opportunity to re-engineer the related processes and redefine roles in ways that make organizations more effective. We study how and when algorithm-enabled process innovation (AEPI) creates value in light of dynamic operational environments (i.e., workload) and behavioral responses to algorithmic predictions (i.e., algorithmic accuracy). Our context is an AEPI effort around a rule-based decision-support algorithm for early detection of sepsis—a costly condition that is the leading cause of death for hospitalized patients. We collaborated with a large U.S.-based hospital system and examined whether AEPI developed for sepsis care (sepsis AEPI) impacts patient mortality and when this impact is stronger or weaker. Methodology/results: We utilize a rich set of clinical and nonclinical data in empirically examining the impact of sepsis AEPI on patient mortality. We leverage the staggered implementation of sepsis AEPI across hospital units and conduct our estimation on a carefully matched sample. The matching utilizes data on patient vitals and the logic behind the algorithm to create a robust comparison group consisting of patient visits for which sepsis AEPI would have triggered an alert if it had been in place. Our empirical analysis shows that sepsis AEPI reduces the likelihood of death from sepsis (45% relative reduction in mortality risk due to sepsis). A higher-than-usual workload and an increase in the average number of inaccurate alert experience at a hospital unit (e.g., an oncology unit, which provides care for cancer patients), in general, reduces the effectiveness of AEPI. We also identify diminishing mortality benefits over prolonged periods of adoption; evaluation of the moderators over time helps explain this diminishing impact. Managerial implications: Our findings suggest that streamlining sepsis-care processes through a predictive algorithm (i.e., algorithm-based monitoring of real-time patient data and providing predictions, streamlined communication channels for coordinating care for a patient with sepsis prediction, and a more standardized process for sepsis diagnosis and treatment) can reduce the loss of life from sepsis. For the 3,739 sepsis patients in our study period, AEPI’s benefits would translate to 181 lives saved. We show that such value, however, is sensitive to operational and behavioral factors as the algorithm becomes a routine part of the day-to-day operations of the hospital. Funding: Financial support from University Hospitals is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1226 .

Rational Design of a Stable Fe‐rich Ni‐Fe Layered Double Hydroxide for the Industrially Relevant Dynamic Operation of Alkaline Water Electrolyzers

AbstractNickel‐iron layered double hydroxides (Ni‐Fe LDHs) consist of stacked Fe3+‐doped positively charged Ni‐hydroxide layers containing charge‐balancing anions and water molecules between the layers. Although Ni‐Fe LDHs are highly active in the oxygen evolution reaction (OER) under alkaline conditions, their poor operational stability remains an issue. Herein, based on density functional theory calculations, it is proposed that the inclusion of a higher Fe content (>40%) than the theoretical Fe3+ limit (≈25%) permitted by Ni‐Fe LDHs can lead to improved structural stability. An Fe‐rich Ni‐Fe LDH electrode is therefore prepared via a growth strategy based on the controlled oxygen corrosion of an Fe substrate, by enabling the incorporation of additional Fe2+ into the Ni2+‐Fe3+ LDH structure. Indeed, microstructural and elemental analysis confirm the presence of additional Fe2+. This Fe‐rich Ni‐Fe LDH electrode not only offers a low OER overpotential (≈270 mV at 200 mA cm−2) but also exhibits an excellent operational stability under dynamic operating environments without any significant performance degradation or metal ion dissolution. Finally, the practical feasibility of the Fe‐rich Ni‐Fe LDH electrode is demonstrated in a single‐cell (34.56 cm2) operation. These findings are expected to aid in the development of reliable OER electrodes for use in commercial water electrolyzers.

Generalizable turbulent flow forecasting for adaptive optics control.

In this work, we characterize the capability of artificial neural network predictive models for generalizable turbulence forecasting, particularly for use in predictive adaptive optics (AO) applications. Predictive AO control, which utilizes future state predictions of an optical wavefront propagated through a turbulent medium to drive correction, is a promising technology for optical propagation in high-disturbance and low-signal environments. The dynamics describing the evolution of turbulent flow can vary greatly. Accordingly, a generalizable approach to turbulence forecasting has key benefits in allowing for prediction across a range of conditions, thus enabling continuous predictive AO operation in dynamic environments and having reduced sensitivity to changes in conditions. We present a model for generalizable turbulence forecasting, which demonstrated consistent high performance over a range of compressible flow conditions outside those included in the training sample, with only a minimal increase in prediction error compared with a hypothetical baseline model, which assumes perfect a priori characterization. These results demonstrate a clear ability to extract useful dynamics from a limited domain of turbulent conditions and apply these appropriately for forecasting, which could inform future design of predictive AO systems.

Design and Fabrication of Autonomous Forklift

Abstract: Material handling is an important activity in factories that will be performed at a lower cost. The use of autonomous robots has the potential to reduce costs while also relieving humans of the physically demanding task of driving forklifts. This paper proposes a mechatronics design and implementation of an autonomous forklift.This paper takes a new look at autonomous forklifts in industrial settings. The proposed system employs advanced perception and control algorithms to enable unmanned operation in dynamic environments. The system employs a combination of sensors and motors to provide accurate situational awareness and obstacle avoidance. The proposed solution aims to increase work efficiency, enhance safety, and minimize the need for human intervention. The experimental results demonstrate that the system is capable of performing complex tasks such as pallet loading and unloading with precision and dependability. By increasing productivity, lowering costs, and improving workplace safety, the proposed model has the potential to have a significant impact on the material handling industry.

Condition-based maintenance for a multi-component system in a dynamic operating environment

Condition-based maintenance for a multi-component system in a dynamic operating environment

A Multivariate Stochastic Degradation Model for Dependent Performance Characteristics

Simultaneous degradation of multiple dependent performance characteristics (PCs) is a common phenomenon for industrial products. The associated degradation modeling is of practical importance yet challenging. The dependence of the PCs can usually be attributed to two sources, one being the overall system health status and the other the common operating environments. Based on the observation, this study proposes a parsimonious multivariate Wiener process model whose number of parameters increases linearly with the dimension. We introduce a common stochastic time scale shared by all the PCs to model the dependence from the dynamic operating environment. Conditional on the time scale, the degradation of each PC is modeled as the sum of two independent Wiener processes, where one represents the common effects shared by all the PCs, and the other represents degradation caused by randomness unique to this PC. An EM algorithm is developed for model parameter estimation, and extensive simulations are implemented to validate the proposed model and the algorithms. For efficient reliability evaluation under a multivariate degradation model, including the proposed one, a bridge sampling-based algorithm is further developed. The applicability and the advantages of the proposed methods are demonstrated using a multivariate degradation dataset of a coating material. Supplementary materials for this article are available online.

Reinforcement Learning Models and Algorithms for Diabetes Management

With the advancements in reinforcement learning (RL), new variants of this artificial intelligence approach have been introduced in the literature. This has led to increased interest in using RL to address complex issues in diabetes management. Using RL, a decision maker (or agent) observes decision-making factors (or state) from the dynamic operating environment, selects actions based on its optimal policy (i.e., a mapping between states and optimal actions), and subsequently receives delayed rewards. The agent adapts its policy to changes in the operating environment to maximize its cumulative reward as time goes by, thereby improving system performance. This paper presents how various variants of RL have been used to improve diabetes management, such as a higher time in range during which the blood glucose level is within the normal range and a higher similarity between RL and physician’s policies. Key highlights focus on the application of RL in diabetes management, including a taxonomy of the attributes of RL (e.g., roles and advantages), essential elements for training (e.g., data and simulators), representations of diabetes attributes in RL models, and variants of RL algorithms. In addition, this paper discusses open issues and potential future developments in the use of RL in diabetes management.

XACML-based semantic rules language and ontological model for reconciling semantic differences of access control rules

Internet of things interconnects increasing numbers of artefacts and individuals, allowing the setup of ambient intelligence systems in multi-domains (e.g., hospitals). Designing a suitable architectural framework is required to ensure the efficiency and sustainability of the implementation of such systems. Consolidating public services about citizens' safety and authorisation decisions when a resource is accessed in an open-dynamic environment are the main challenges that can be highlighted. Therefore, the semantic heterogeneity between the local policies of the different domains is a crucial lock for implementing this process. Our approach goes beyond the semantic web languages' weaknesses by combining the XACML-based security policy model with a semantic rules language developed during the European SembySem Project. Built on top of RDF(S), the proposed model aims to abstract the security implementation, reconcile semantic differences across multi-domain, and maintain the local security policy. Moreover, this model addresses the semantic heterogeneity of sensors' data during knowledge-sharing.

Cognitive Adaptability and Sales Growth of Selected Small and Medium Scale Enterprises in Lagos States, Nigeria

Stament of the Problem: Small and Medium Enterprises (SMEs) contribute immensely to the development of any nation’s GDP, of which Nigeria is not exempt. However, the survival rate has become very low, and the reason for the high mortality rate has been attributed to the volatility, uncertainty, complexity, and ambiguity of the dynamic operating environment. Responding with strategic decisions to changing environmental factors is a function of how cognitively adaptable the decision-makers are. Objective: Hence, the study investigated the effect of cognitive adaptability and business survival on selected small-scale enterprises in Lagos State, Nigeria. Methodology: The study adopted a survey research design. and simple ransampling technique. . The study population was 42,067 owners/managers of the registered SMEs in Lagos State, Nigeria. The sample size was 770 respondents, and this was determined using the Cochran sample size formula. A structured, adapted and validated questionnaire was administered. Cronbach’s alpha coefficient for the construct ranged from 0.725 to 0.923. Data were analysed using the descriptive and inferential (Multiple linear and hierarchical regression) statistics Findings: The regression model indicates that if cognitive adaptability factors were held constant at zero, the sales growth of the selected Small and Medium Scale Enterprises in Lagos State, Nigeria would be 8.479. In the predictive model it is seen that one of the variables of cognitive adaptability is insignificant, therefore the management of small and medium scale enterprises can downplay the variable that is why it is not included in the prescriptive model. From the analysis, the prescriptive models show that dimensions of cognitive adaptability (goal orientation, metacognitive knowledge, metacognitive control, and monitoring) except metacognitive experience have a significant effect on sales growth. This means that companies in the SMEs in Lagos State should pay close attention to the three components of cognitive adaptability in order to enhance sales growth. Conclusion: The study concluded that the owner and manager should take cognizance of cognitive adaptability, which is tutorial in nature, to enable them to acquire needed skills and plan their tasks creatively. Primarily to develop the capacity to respond with effective and strategic decisions that will aid their survival in the disruptive and dynamic business environment.