Uncertainty In Behavior Research Articles

Building energy consumption under occupants’ behavior uncertainty in pre and post-renovation scenarios: a case study in Italy

In Europe, the energy renovation of the existing building stock is a great opportunity to significantly reduce energy consumption and greenhouse gas (GHG) emissions and reach the European sustainability targets. In this framework, building energy simulations (BES) tools are very useful in verifying energy retrofit measures' effectiveness and compliance with national standards. However, an inaccurate numerical prediction, the so-called “performance gap” between measured and numerical performance, is often obtained, mainly due to the inherent uncertainty of model input. Due to its stochastic nature, the occupants’ behavior (OB) is considered among the key contributors to this gap. However, the most recent Building Energy Model (BEM) approaches adopt deterministic hourly-defined profiles for characterizing OB, thus neglecting the related uncertainty. In this work, the impact of OB uncertainties on energy consumption (EC) prediction is evaluated by adopting a Karhunen-Loève Expansion sampling technique, used to randomly perturb OB profiles such as heating setpoint (HS), internal thermal loads (IL), and windows opening (NV). Two BEMs of a typical Italian residential building in pre- and post-renovation scenarios are considered and calibrated on real EC data. The results demonstrated that HS uncertainty has the highest impact on EC in all scenarios. Moreover, the higher the energy performance of the building, the higher the impact of OB, especially for IL and NV patterns.

Analytical method to improve the decision-making criteria approach in managing digital social channels

This study explores the decision-making process involved in managing digital communities in the digital social channel ecosystem, specifically in the context of antagonistic digital communication and harmful content dissemination. The rise of digital communities in social media has changed the way people interact with each other, form communities, and participate in social activities. However, these interactions can also lead to conflicts, and the dissemination of harmful content can pose a risk to users' information security. This research proposes a method to mitigate these issues and optimize efficacy while minimizing potential hazards. The proposed method is based on the analysis of the information situation in the management of digital communities under conditions of complete uncertainty, antagonistic behaviour, and partial uncertainty. The study examines the decision-making process involved in antagonistic interaction among users of digital social channels. The methods proposed in this research were implemented to prevent the adverse consequences of community conflicts and the dissemination of potential risks to the security of users' information through prompt and efficacious decision-making. The proposed strategy optimizes efficacy through the implementation of the Wald criteria while minimizing potential hazards by adhering to the Savage criteria. Additionally, the Hurwitz criteria, which presents a balanced approach incorporating both the maximum and minimum criteria, was also employed. The methods were tested within the online community present on the social media platform, Facebook social networks, and the results demonstrate that the suggested technique improves the effectiveness and validity of managers' decisions in digital communities. The study also highlights the use of social networks and the virtual sphere as tools for hybrid warfare on the internet, which can be effective. Effective community management is necessary to mitigate the negative effects of online interactions, and the proposed method offers a valuable tool for managers to make informed decisions and prevent adverse consequences.

Model free Reinforcement Learning to determine pricing policy for car parking lots

Finding a parking space has not only become painful but also costs a lot in most of the metropolitan cities. With the increase in number of vehicles and limited resources such as manpower and space, the need for effective management of parking lots has increased. Improper management of parking lots can have negative consequences such as traffic congestion, wastage of time in search of parking spaces, air pollution and even loss of revenue for the parking lot managers. Dynamic pricing is a powerful tool to control the behavior of drivers by diverting them towards the unoccupied and cheaper parking lots. Though there are several existing dynamic pricing strategies, determining the right prices is quite challenging due to lack of knowledge of drivers’ behavior and several uncertainties like harsh weather and special days. In this paper Reinforcement Learning(RL) technique called Q-learning is used to calculate the dynamic prices for parking lots on hourly basis without the need of prior information about the system. Crucial factors like distance of the parking lots from the city centers, weather and holidays are considered in the proposed algorithm to achieve better accuracy. Price Elasticity of Demand (PED) is used in the proposed work to calculate the new state(occupancy) when an action(dynamic price charged by the parking lot owner) is taken place. Hourly prices are estimated using the proposed algorithm and simulation results show that the calculated prices can efficiently manage parking occupancy during peak and off peak hours. The simulation output also shows that the proposed algorithm can successfully increase the revenue of the parking lot owners.

Parsing through paradigms: uncertainty and decision-making in human information behavior

PurposeThis study demonstrates how individual paradigms implicate the questions asked, methods used and results drawn in association with a common object of study in human information behavior (HIB) research – the relationship between uncertainty and decision-making.Design/methodology/approachThe author uses textual case studies to examine uncertainty and decision-making through the framework of four paradigms used in HIB research: positivism, cognitivism, collectivism and constructionism and suggests deconstructionism as a paradigm which raises new questions around this topic.FindingsPositivistic approaches to uncertainty are often systems oriented; cognitive approaches are often user-oriented; collectivist approaches are intersubjective; and constructionist approaches blend a subjective and intersubjective research orientation. Deconstructionism raises new questions around ethics and responsibility in relation to decision-making, and the author therefore situates it as a new paradigmatic approach for this topic in HIB research.Originality/valueDespite the presence of research aimed at recognizing and defining paradigms in HIB research, a comparative micro-examination of how individual paradigms implicate a specific research topic has yet to be conducted. Each paradigm uniquely shapes the ways in which uncertainty and decision-making are characterized, but the four central ones examined here have thus far left out questions of ethics and responsibility as being core elements of decision-making as tied to uncertainty. Therefore, this paper introduces deconstructionism as a paradigm new to HIB uncertainty research, arguing that it provides an important and novel complication of existent research questions and approaches.

Structural strengthening of a constructed timber bridge: Field application and numerical modeling

This paper presents the behavior of a timber bridge strengthened with lag bolts, carbon fiber reinforced polymer (CFRP) sheets, and hollow structural sections (HSS). The 83-old bridge is composed of three spans, supported by 14 Douglas Fir girders, and is tested employing truck loadings of 120 kN and 267 kN. Three-dimensional finite element models are formulated to study the flexural response of the bridge with and without the retrofit options. The magnitude of the truck load dominates the degree of dispersion in girder deflections and alters the shape of probability density functions, which are associated with behavioral uncertainty. The effectiveness of strengthening is apparent in terms of reducing the exceedance probability of deflection limits and increasing reliability indices above the design threshold of β = 3.5, stipulated in bridge specifications. Among the three methods, the stiffening efficiency of the HSS option is higher at the system level; however, the use of CFRP is most efficient at the member level. Live load distribution factors are examined and implementable recommendations are provided for practice. Parametric investigations clarify the efficacy of variable HSS sizes and the ramifications of a permit truck weighing 445 kN.

Optimization of the electricity consumption behaviors of users under uncertain electricity prices and consumption patterns

Real-time electricity prices and customer demands are inextricably linked in the smart grid environment. For the purpose of reducing grid load peaks, enhancing grid security, and optimizing customers’ electricity bills, it is critical to model dynamic real-time electricity prices and flexible customer requests, along with providing an optimized plan to guide users’ decisions regarding the use of household appliances and quantify their needs. The solution to this problem, however, is not straightforward due to the following factors. Firstly, the real-time electricity price is constantly changing, so it must be estimated from grid data associated with it. Furthermore, there are often some variations in the actual power consumption of appliances due to the uncertainty of human behavior, the characteristics of appliances, and the influence of the environment. In addition, when optimizing, serious problems such as tripping and circuit overload must be taken into account. Therefore, considering random electricity prices and random electricity consumption, in the paper, we present the models to simulate real-time changes in electricity prices and energy consumption behaviors, as well as a method to help users optimize their use of household appliances and quantify their requirements with lower electricity cost. By analyzing the experimental results, we demonstrate that our work can be used to provide a scheduling scheme for appliances that minimizes electricity costs and reduces grid pressure.

Islamic Elementary School Teachers’ Uncertainty on Distance Learning

Various obstacles were encountered by teachers during distance learning when covid-19 pandemi hit. Online teaching has become tough challenge for the forest edge villages’ school teachers in Madiun regency, Indonesia. Online teaching obstacles were also felt by the first-grade teachers at Madrasah Ibtidaiyyah Plus Al-Makmun (MI Plus Al-Makmun) which caused uncertainty. This study aims to examine the types of the teachers’ uncertainty and their uncertainty reduction strategies using uncertainty reduction theory. Descriptive qualitative method was used in this study and in-depth interview was conducted to gather data. The results of this study indicate that there are two types of uncertainty felt by the informants, the first is cognitive which is related to difficulties in understanding their students’ character and their insecurity about students’ understanding of school lessons, and the second is behavioral uncertainty which is related to how students respond and behave in learning process. Meanwhile, the teachers’ uncertainty reduction strategy are (1) passive strategies through secret observation, (2) active strategies by seeking information through parents and fellow teachers, and (3) interactive strategies by communicating directly with students

Uncertainty Analysis of Creep Behavior of Compacted Loess and a Non-Deterministic Predication Method for Post-Construction Settlement of a High-Fill Embankment

Property of geotechnical materials has inherent uncertainty due to the complex formation process and inevitable test error. However, existing long-term deformation prediction methods for geotechnical structure such as a filling embankment are deterministic, which ignores the uncertainty of soil property. In this study, the uncertainty of creep behavior of compacted loess was investigated through repetitive creep tests and statistical analysis. Five different loading levels and two loading modes were considered in the tests. The creep test was repeated 45 times for each condition. Through a statistical analysis for the test results, a modified Merchant creep model was established to improve the accuracy of long-term deformation prediction. An empirical transformation equation between staged loading and separated loading mode of the creep test results was also introduced to improve applicability of the method. On this basis, a non-deterministic predication method for post-construction settlement of loess fill embankment was proposed. Furthermore, the proposed method was applied to the prediction of the post-construction of a 61.5 m loess filling embankment. The measured on-site post-construction settlement value falls within the 95% confidence interval of the predicted range which proves the efficiency and practicability of the proposed non-deterministic predication method. Compared to deterministic methods, the proposed method can describe the predicted deformation in a probabilistic way in the form of contour plot. The proposed method provides a basic approach for the probabilistic design and reliability assessment of filling engineering.

Examining the impact of resilience strategies in mitigating medicine shortages in the United Kingdom's (UK) pharmaceutical supply chain (PSC)

PurposeMedicine shortages have a detrimental impact on stakeholders in the pharmaceutical supply chain (PSC). Existing studies suggest that building resilience strategies can mitigate the effects of these shortages. As such, this research aims to examine whether resilience strategies can reduce the impact of medicine shortages in the United Kingdom's (UK) PSC.Design/methodology/approachA sequential mixed-methods approach that involved qualitative and quantitative research enquiry was employed in this study. The data were collected using semi-structured interviews with 23 key UK PSC actors at the qualitative stage. During the quantitative phase, 106 respondents completed the survey questionnaires. The data were analysed using partial least square-structural equation modelling (PLS-SEM).FindingsThe results revealed that reactive and proactive elements of resilience strategies helped tackle medicine shortages. Reactive strategies increased relational issues such as behavioural uncertainty, whilst proactive strategies mitigated them.Practical implicationsThe findings suggest that PSC managers and decision-makers can benefit from adopting structural flexibility and proactive strategies, which are cost-effective measures to tackle medicine shortages. Also engaging in strategic alliances as a proactive strategy mitigates relational issues that may arise in a complex supply chain (SC).Originality/valueThis study is the first to provide empirical evidence of the impact of resilience strategies in mitigating medicine shortages in the UK's PSC.

Safe, learning-based MPC for highway driving under lane-change uncertainty: A distributionally robust approach

We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users. The dynamics of road users are modeled using Markov jump systems, in which the switching variable describes the desired lane of the vehicle under consideration and the continuous state describes the pose and velocity of the vehicles. We assume the switching probabilities of the underlying Markov chain to be unknown. As the vehicle is observed and thus, samples from the Markov chain are drawn, the transition probabilities are estimated along with an ambiguity set which accounts for misestimations of these probabilities. Correspondingly, a distributionally robust optimal control problem is formulated over a scenario tree, and solved in receding horizon. As a result, a motion planning procedure is obtained which through observation of the target vehicle gradually becomes less conservative while avoiding overconfidence in estimates obtained from small sample sizes. We present an extensive numerical case study, comparing the effects of several different design aspects on the controller performance and safety.

Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning

Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes decision-making a challenging task in an unsignalized intersection scenario. In this paper, a decentralized multi-agent proximal policy optimization (MAPPO) based on an attention representations algorithm (Attn-MAPPO) was developed to make joint decisions at an intersection to avoid collisions and cross the intersection effectively. To implement this framework, by exploiting the shared information, the system was modeled as a model-free, fully cooperative, multi-agent system. The vehicle employed an attention module to extract the most valuable information from its neighbors. Based on the observation and traffic rules, a joint policy was identified to work more cooperatively based on the trajectory prediction of all the vehicles. To facilitate the collaboration between the vehicles, a weighted reward assignment scheme was proposed to focus more on the vehicles approaching intersections. The results presented the advantages of the Attn-MAPPO framework and validated the effectiveness of the designed reward function. Ultimately, the comparative experiments were conducted to demonstrate that the proposed approach was more adaptive and generalized than the heuristic rule-based model, which revealed its great potential for reinforcement learning in the decision-making of autonomous driving.

A multiple spatio‐temporal features fusion approach for short‐term passenger flow forecasting in urban rail transit

AbstractTimely and precise short‐term passenger flow forecasting (STPFF) is a key to intelligent urban rail transit (URT) operation. The uncertainty of passenger travel behaviors leads to passenger flow data showing high nonlinearities and complex spatio‐temporal correlation. Therefore, the ability to capture such correlations determines the prediction performance of the model. However, current methods either consider only the physical topology of URT network or lack the ability to model global spatial dynamics, making it difficult to obtain satisfactory predictions. Therefore, a Multiple Spatio‐Temporal Features Fusion (MSTFF) network is proposed for STPFF in URT networks. Specifically, the connectivity and passenger flow similarity relationships in URT networks are encoded as physical adjacency graph and virtual functional similarity graph, respectively, and then these two complementary graphs are input into two designed Residual Graph Convolutional Gated Recurrent Unit (RGCGRU) modules to extract their spatio‐temporal features. Second, an Attention‐based Gated Recurrent Unit (AGRU) module is designed to capture global dynamic evolutionary trends in both spatial and temporal dimensions. Finally, a fully connected neural network is used to fuse these features. Experiments on two real passenger flow datasets of URT show that MSTFF performs well on various prediction tasks (15, 30, 45, and 60 min).

Modeling Interactions of Autonomous/Manual Vehicles and Pedestrians with a Multi-Agent Deep Deterministic Policy Gradient

This article focuses on the development of a stable pedestrian crash avoidance mitigation system for autonomous vehicles (AVs). Previous works have only used simple AV–pedestrian models, which do not reflect the actual interaction and risk status of intelligent intersections with manual vehicles. The paper presents a model that simulates the interaction between automatic driving vehicles and pedestrians on unsignalized crosswalks using the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The MADDPG algorithm optimizes the PCAM strategy through the continuous interaction of multiple independent agents and effectively captures the inherent uncertainty in continuous learning and human behavior. Experimental results show that the MADDPG model can fully mitigate collisions in different scenarios and outperforms the DDPG and DRL algorithms.

Perceived transaction cost and its antecedents associated with fintech users' intention: Evidence from Pakistan

Researches depict a considerable degree of acknowledgment of financial technology improvement in Pakistan. However, the costs blocking clients' intention to utilize financial technology remain dubious. Building upon Transaction Cost Economics and Innovation diffusion theory, this paper hypothesizes that consumers' transaction cost of fintech is affected by nine antecedents: perceived asset specificity, complexity, product uncertainty, behavioral uncertainty, transaction frequency, dependability, limitations, convenience, and economic utility. Transaction cost has a negative relationship with consumers' intentions to use fintech for online buying or availing services. We tested the model using data gathered from the individuals. The results show that factors that are positively related to consumers' perceived transaction cost, among them product uncertainty (β = 0.231) is the greatest of factors, followed by behavior uncertainty (β = 0.209) and asset specificity (β = 0.17), those that are negatively associated are dependability (β = 0.11), and convenience (β = 0.224). The study is limited in scope, focusing primarily on cost factors. Future research may analyze additional cost-related elements and the actual use of financial technology by using samples from different countries.

Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition.

This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.

A dynamic probabilistic risk assessment platform for nuclear power plants under single and concurrent transients

ABSTRACT Dynamic probabilistic risk assessment (DPRA) of nuclear power plants (NPPs) has become one of the most critical research areas, especially in the aftermath of the 2011 Fukushima Daiichi nuclear accident. Uncertainty in NPP behavior is key when considering its safety under different operating conditions. Such uncertainty typically results from operation parameters, system conditions, and modeling assumptions. This study integrates the system dynamics (SD) modelling approach with an uncertainty analysis method to quantify the dynamic probabilistic risk in NPPs. To demonstrate the approach’s applicability, the average fuel temperature is used to estimate the probability of reactor core damage under different transients, representing perturbations in reactivity and steam valve coefficient. A Monte Carlo simulation is employed to investigate the effect of uncertainties associated with the different model parameters. A global sensitivity analysis demonstrates that the total delayed neutron fraction, the heat transfer coefficient from fuel to coolant, the coolant temperature coefficient of reactivity, and the fuel temperature coefficient of reactivity are the primary controllers of the plant response variability under the transients considered. In summary, the integration of SD modelling and uncertainty analysis presents an effective DPRA approach that overcomes the limitations of static counterparts while minimizing the computational resources required.

Economic scheduling of virtual power plant in day-ahead and real-time markets considering uncertainties in electrical parameters

The virtual power plant (VPP) concept, which offers energy efficiency, benefits significantly from the tremendous potential of integrating distributed energy resources (DERs). This work proposes optimal economic dispatch of virtual power plant (VPP), and the problem is formulated in a novel way considering the potential of energy storage systems (ESS) of electric vehicles (EVs) and data center (DC) in a data center facility test model using optimized energy management system (EMS). The problem is evaluated for maximizing the revenue for VPP considering market prices and the uncertainties in the distributed energy resources. To this purpose, a techno-economic analysis of the proposed test system of VPP is evaluated using mixed-integer linear programming (MILP). Renewable generation, electricity price, load demand, and real-time EV behavior uncertainty analysis are forecasted using Long Short-Term Memory (LSTM) along with DC battery degradation analysis which constitutes the optimization model. The results showed that the VPP EMS can perform optimal power scheduling with the day ahead and real-time electricity price scenarios. Moreover, further analysis including battery degradation cost of DC UPS ESS proved the capability of the proposed framework by up to 2064.4 $/h revenue for day-ahead price and 562 $/h revenue for the real-time price compared to revenue without battery degradation cost. A simulation scenario is also carried out where the constant load demand and the battery storage powers are varied between 50% and 150%. It can be concluded that the 500 kW and 750 kW ESS (100% and 150%) are better choices as compared to 250 kW (50%) during increased load demand for investors due to their higher revenue. Hence it is concluded that this research highlights the importance of revenue benefits from deploying VPP with the collaborative operation of DERs.

Performance-Based Characterization and Quantification of Uncertainty in Damage Plasticity Model for Seismic Fragility Assessment of Concrete Structures

Seismic probabilistic risk assessment requires explicit consideration and quantification of all sources of uncertainty. Although uncertainties in earthquakes and basic material properties, such as nominal concrete compressive strength, strain at maximum stress, and modulus of elasticity of concrete, have been considered in some studies, uncertainties in parameters used to model the nonlinear behavior of concrete are often not considered. Recent recommendations in design standards have incorporated performance-based limit states in the design and risk assessment of nuclear power plants. These recommendations have implicitly mandated that the uncertainty in the nonlinear behavior of concrete needs to be characterized and quantified in accordance with different performance limit states. This study focused on nonlinear model parameters in the concrete damage plasticity model (CDPM) and their characterization in terms of material strength parameters. Furthermore, a performance-based characterization and quantification of uncertainties in nonlinear model parameters was also conducted. Data from existing experimental studies were used to study the uncertainties in CDPM and were used as a basis for the proposed quantification.

Imitation-Based Motion Planning and Control of a Multi-Section Continuum Robot Interacting With the Environment

Recently, flexible robots are growing in importance owing to their merits over rigid robots in maneuverability and safety, which equips them to work in unstructured environments, such as occur in medical applications. However, motion planning and control of flexible manipulators is challenging due to their compliance behavior and system uncertainties. Thus, this letter presents an Imitation-based Motion Planning (IbMP) approach, along with dynamic impedance control for learning, planning, and trajectory tracking of a two-section soft continuum robot in a dynamic environment. Point-to-point motion demonstrations, including the robot's tip position and orientation are intuitively provided by a motion capture system (OptiTrack V120-trio) and a similar kinematic flexible interface. Additionally, a singularity-free dynamic model based on Lagrangian formulation and Taylor expansion series of a two-section continuum robot is derived while planning for robot motions. The simulation results show that the IbMP approach, along with the dynamic impedance control, generalizes the robot's motion by varying the initial and goal of the robot's tip pose while avoiding static and dynamic obstacles, and moving back to the desired track after disturbances. Finally, the stability and performance of the IbMP algorithm against input disturbances are assessed using a Monte Carlo approach that can guide the selection of gain values.

Human Activity Recognition using Deep Learning

Human activity recognition (HAR) is a difficult task that entails recognizing and categorizing human actions using sensor data gathered by mobile phones and wearable. Because of recent developments in deep learning, deep neural networks (DNNs) have surpassed other methods as the gold standard for HAR. In this study, we provide a high-level introduction to HAR by making use of deep learning methods. We begin with an overview of the HAR problem and its obstacles, such as the inherent uncertainty of human behaviour and the enormous dimensionality of sensor data. Then, we talk about how deep learning plays a part in HAR and provide a thorough assessment of recent studies that have used several kinds of deep learning architectures, such as CNNs, RNNs, and hybrid architectures, to solve HAR problems. We also discuss some of the important issues and on-going research questions in HAR utilizing deep learning, including how to handle imbalanced datasets, how to interpret DNN models, and how to cope with the sparsely and variability of sensor data. We wrap up by talking about where this type of deep learning-based HAR could go in the future, including applications like personalised care monitoring, activity- based intelligent assistant, and smart home automation.