Operator Performance Research Articles

A Retrospective Regression Analysis on the Dynamic Shooting Scores of SWAT Operators

ABSTRACT Introduction Special weapons and tactical (SWAT) operators need to perform intense physical activity and fire their weapons with accuracy. Sport performance attributes have been found to affect the performance of SWAT operators during physical assessments, but little research has been devoted to dynamic marksmanship. This study evaluated the relationship between age, personal shooting practice, years of law enforcement experience, and years of SWAT experience with dynamic shooting accuracy. Methods SWAT operators (n = 195) were grouped by age, self-report of previous experience in law enforcement and SWAT, and shooting practice behaviors (rounds fired per month). Operators completed several dynamic shooting drills (e.g., sprinting, pro-agility, jumping) in which they fired three rounds upon completion. The accuracy and time it took to fire all shots were used to calculate a dynamic shooting score for each firearm (pistol and rifle) for each assessment. A regression analysis was completed to assess the impact of age, experience, and shooting practice on dynamic shooting. Results As compared to the ≤30-yr-old age group, each of the older age groups showed a decrease in overall shooting score on average. Compared to those with ≤5 yr of SWAT experience, both groups with more experience showed an increase in overall shooting score on average. Those firing more than 250 rounds monthly (compared to ≤250) showed an increase in overall shooting score on average. Years in law enforcement was not found to be associated with overall shooting score. Conclusion SWAT experience and rounds fired per month were found to have the most impact on dynamic shooting performance. Early identification of officers to be brought into special operations would allow them to gain experience while younger, optimizing factors for dynamic marksmanship. Operators should maximize both monthly practice with their primary weapon and dynamic movement training to optimize development of accurate dynamic shooting capabilities.

Operator Augmentation for Improving Human Performance of Tasks With Uncertainties

Frazing is a manual task in turbine blade manufacturing companies, in which the operators must discover and repair the defects of the workpieces. Such detection is so attention-demanding and time-consuming that the operators sometimes miss the defects, resulting in poor human performance and quality. Hence, this study aims to guide the operators with a systematic visual search using augmented reality (AR). For this purpose, an AR-based assistance system was built. To evaluate the system’s usability and effectiveness, a user study was conducted on 24 skilled operators. The results showed that the performance of the augmented operators was enhanced as the task completion time and detection misses decreased using the AR tool by 11.6 and 100 percent, respectively. The mental workload declined significantly after using the AR system. The most prominent finding of this research is that the AR tool was quite effective in reducing mental fatigue and workload, leading to improved human performance in ever-changing tasks.

The Effect of Informal Food Service Operators’ Capabilities on Their Performance in Nigeria

Technology has become crucial for businesses of all scales, enhancing operational efficiency, customer experience, and overall performance. While many formal businesses have embraced technology, small and informal businesses, particularly in rural areas, often struggle with adoption due to resource constraints. This study examines the impact of technological capability (TC) on the performance of informal food service operators (IFO) in southwestern Nigeria. This study employed a quantitative approach whereby a closed-ended questionnaire was administered to 180 IFOs across different LGAs in Oyo State to elicit information about the effect of their activities on performance indicators. The response rate was 93.8%. Data was analyzed using a binary logistic regression model. Findings revealed that the innovation capability (β—1.657; p—0.003*) and production capability (β—3.276; p—0.009*) of the IFOs significantly influenced their sales turnover and profit level is influenced by innovation capability (β—1.657; p—0.013*), investment capability (β—2.013; p—0.004*) and linkage capability (β—2.716; p—0.003*) within the study area. The study recommends that there should be strong linkages between the operators and technology-providing organisations to enhance their innovation capability.

Neural network procedures for the cholera disease system with public health mediations

Severe gastrointestinal infections and watery diseases like cholera are still a major worldwide medical concern in the developing nations. A mathematical system contains some necessary dynamics based on the cholera spread to investigate the influence of public health education movements along with treatment and vaccination as control policies in restraining the infection. The cholera disease system with public health mediations divide the density of human population into seven categories based on the status of diseases, who are susceptible, educated, vaccinated, quarantined, infected, treated and removed individuals along with the aquatic bacteria population. The motive of current research is to present the numerical performances of the cholera disease system with public health mediations by using a stochastic computing process based on the Bayesian regularization neural network. A data is constructed by using a conventional Adam scheme that reduces the mean square error by distributing the data into training, validation and testing with some reasonable percentages. Twenty-five neurons, and sigmoid fitness function are used in the stochastic process to solve the model. The accuracy is justified by using comparison of the results, absolute error around 10-06 to 10-08 and some statistical operator performances.

Operator learning with Gaussian processes

Operator learning focuses on approximating mappings G†:U→V between infinite-dimensional spaces of functions, such as u:Ωu→R and v:Ωv→R. This makes it particularly suitable for solving parametric nonlinear partial differential equations (PDEs). Recent advancements in machine learning (ML) have brought operator learning to the forefront of research. While most progress in this area has been driven by variants of deep neural networks (NNs), recent studies have demonstrated that Gaussian process (GP)/kernel-based methods can also be competitive. These methods offer advantages in terms of interpretability and provide theoretical and computational guarantees. In this article, we introduce a hybrid GP/NN-based framework for operator learning, leveraging the strengths of both deep neural networks and kernel methods. Instead of directly approximating the function-valued operator G†, we use a GP to approximate its associated real-valued bilinear form G˜†:U×V∗→R. This bilinear form is defined by the dual pairing G˜†(u,φ)≔[φ,G†(u)], which allows us to recover the operator G† through G†(u)(y)=G˜†(u,δy). The mean function of the GP can be set to zero or parameterized by a neural operator and for each setting we develop a robust and scalable training mechanism based on maximum likelihood estimation (MLE) that can optionally leverage the physics involved. Numerical benchmarks demonstrate our method’s scope, scalability, efficiency, and robustness; showing that (1) it enhances the performance of a base neural operator by using it as the mean function of a GP, and (2) it enables the construction of zero-shot data-driven models that can make accurate predictions without any prior training. Additionally, our framework (a) naturally extends to cases where G†:U→∏s=1SVs maps into a vector of functions, and (b) benefits from computational speed-ups achieved through product kernel structures and Kronecker product matrix representations of the underlying kernel matrices.11GitHub repository: https://github.com/Bostanabad-Research-Group/GP-for-Operator-Learning.

Impact of selective environmental sound attenuation on operator performance, stress, attention, and task engagement in teleoperated demolition

The noise produced in demolition sites can mask safety-critical sounds that inform operators about task conditions and hazards. These problems are exacerbated in teleoperated demolition, where the separation between operator and site compromises operators' situation awareness and cognitive loads. This paper assessed the effects of environmental sounds with and without attenuation on the operators' performance and response (e.g., stress, attention, task engagement) during teleoperated demolition. Eighty participants completed three virtual demolition tasks under different environmental sound conditions, i.e., no sound (NS), unfiltered sound (US), and filtered sound (FS) with 20-dB attenuation of background noise and robot's sounds to allow focus on safety and task conditions. The results show that US induced more stress than NS and FS. Also, FS resulted in fewer collisions, faster reaction times, and greater attention and task engagement than US. These results can support the design of sound feedback interfaces for teleoperation in construction.

An authorization framework to mitigate insider threat in CIM-based smart grid

A smart grid (SG) is based on integrated data from distributed information systems, and the common information model (CIM) provides standard data infrastructure. In the SG, a malicious insider operator can lead to widespread failures in the power system by disrupting the system processes. The severity of the attack increases when he/she can access integrated data with legal permissions and steal, delete or modify them. This paper proposes an authorization framework to mitigate data access permissions of an insider operator who does not perform its duties properly in a CIM-based SG. In the proposed method, the accessibility of a CIM class is determined based on the operator trust and the criticality level of the issued SQL command. The value of the operator trust is calculated using its performance periodically or when an anomaly is detected. The proposed method is also able to detect anomalies in operator performance.

Extended Fourier Neural Operators to learn stiff chemical kinetics under unseen conditions

The solution of stiff chemical kinetics is recognized as the computational bottleneck for direct simulations of reacting flows. In this study, we extend the concept of Fourier Neural Operator (FNO) to learn stiff chemical kinetics. Specifically, element and mass conservation are introduced as the physical constraints in the extended FNO (EFNO). In addition, the training data are transformed by a Box–Cox strategy to rectify the skewed distribution of the species in the stiff problems. Finally, balanced loss functions are formulated to address the unbalanced sampling data points in complex reacting flow problems. The EFNO model is leveraged to forecast the temporal evolution of chemical species, utilizing an iterative approach wherein the prediction outcome from the previous time step is employed as a new input for subsequent time step prediction. The results in this work demonstrate the significant use of an EFNO approach to solving stiff chemical dynamics in reacting flow simulations, with a time step size comparable to the typical flow time step size. Its prediction accuracy and generalization ability are evaluated by comparing with the original FNO, Deep Nueral Network (DNN) and DeepONet models, in solving toy problems, zero-dimensional hydrogen autoignition, and a three-dimensional hydrogen/ammonia turbulent jet flame. The EFNO is shown to be highly accurate. More importantly, compared with other deep learning models, it can be generalized to stiff chemical kinetic states under unseen conditions, which the model has never trained for. The great performance of EFNO in terms of accuracy and generalization ability suggests that EFNO is a promising solution algorithm for stiff chemical kinetics problems in reacting flows.Novelty and Significance Statement: The novelty of this work lies in the newly developed extended Fourier neural operators (EFNO) to learn stiff chemical kinetics. Specifically, we for the first time evaluated and tested the performance of Fourier neural operators in solving stiff chemical kinetics. More importantly, we extended the original Fourier neural operators to accurately solve for stiff chemical kinetics problems under unseen conditions, which was a very challenging problem for deep learning methods in the literature. Our results demonstrated that the EFNO model solves chemical kinetics in both simple 0D autoignition and complex 3D turbulent jet flames with great accuracy and generalization ability, even for conditions which the training dataset has never encompassed. This work is significant because it developed a neural operator-based algorithm that can significantly accelerate the stiff chemical kinetic solution process in reacting flow simulations with great accuracy even for unseen initial conditions.

PICL: Physics informed contrastive learning for partial differential equations

Neural operators have recently grown in popularity as Partial Differential Equation (PDE) surrogate models. Learning solution functionals, rather than functions, has proven to be a powerful approach to calculate fast, accurate solutions to complex PDEs. While much work has been performed evaluating neural operator performance on a wide variety of surrogate modeling tasks, these works normally evaluate performance on a single equation at a time. In this work, we develop a novel contrastive pretraining framework utilizing generalized contrastive loss that improves neural operator generalization across multiple governing equations simultaneously. Governing equation coefficients are used to measure ground-truth similarity between systems. A combination of physics-informed system evolution and latent-space model output is anchored to input data and used in our distance function. We find that physics-informed contrastive pretraining improves accuracy for the Fourier neural operator in fixed-future and autoregressive rollout tasks for the 1D and 2D heat, Burgers’, and linear advection equations.

Teleoperated forwarder with VR: impact of latency on operator log handling performance

ABSTRACT This study uses a full scale cut to length forwarder prototype to explore crane log handling during teleoperation (remote-control). Visual feedback is provided from stereo cameras into a VR headset. Details of the system are provided along with measurements on key properties. The system allows for an artificial latency (the delay between a command and the observed response) to be introduced. A setup to allow repeated crane operations is used to evaluate operator performance at different latencies and also to compare teleoperation with in-machine operation. The results indicate that teleoperation of crane log handling can achieve comparable performance to traditional in-machine operation. Notably, the performance is consistent even at transmission latencies that surpass those expected in 4 G internet connections. This suggests a potential for remotely operating forwarders from both on-site and potentially off-site locations. This research did not delve into actual forwarding work or the implications of wireless connection bandwidth or alternative technologies like Wi-Fi or 5 G. Future investigations should consider these factors, along with the requirement for enhanced camera systems to ensure holistic machine visibility and consequently, safe teleoperation.

Підходи до адаптації налаштувань поля зору операторів під час тренування та експлуатації дронів

Introduction. The widespread use of unmanned aerial vehicles (UAVs), including first-person view drones (FPV drones), makes it more urgent to create modern training complexes for training UAV operators and ensuring their optimal performance. The purpose of the paper is to investigate ways and methods of using artificial intelligence to reduce the load on the vestibular apparatus of external pilots of FPV drones. Results. Various factors influencing the operator's vestibular apparatus have been studied, especially those that allow the use of artificial intelligence (AI) tools. The analysis of a group of software and hardware factors and separately the means of adjusting the operator's working field of vision is relevant. The proposed method is effective both during operation and during training on training software and hardware complexes, where this is especially important, since operators train extremely intensively. Conclusions. Individual adaptation of field of view settings using AI methods has significant potential to improve the efficiency and comfort of external pilots of FPV drones, although there are certain shortcomings and limitations that should be taken into account. Ensuring data confidentiality, managing computing resources and algorithm efficiency are key aspects that require attention for the successful implementation of such solutions. It is possible to ensure optimal operator performance when performing tasks that require vestibular functions. This will reduce the requirements for external pilots and, accordingly, increase the number of people capable of controlling FPV drones. Methods for using AI to optimize the operator's field of view work successfully both during the operation of real drones and on training software and hardware complexes. Keywords: FPV drone, training complex, unmanned aerial vehicle, external pilot, operator, virtual reality, artificial intelligence, method, visual-vestibular conflict, operator` field of view, optimal operator performance.

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

Physiological Predictors of Operator Performance: The Role of Mental Effort and Its Link to Task Performance.

The present study investigated how pupil size and heart rate variability (HRV) can contribute to the prediction of operator performance. We illustrate how focusing on mental effort as the conceptual link between physiological measures and task performance can align relevant empirical findings across research domains. Physiological measures are often treated as indicators of operators' mental state. Thereby, they could enable a continuous and unobtrusive assessment of operators' current ability to perform the task. Fifty participants performed a process monitoring task consisting of ten 9-minute task blocks. Blocks alternated between low and high task demands, and the last two blocks introduced a task reward manipulation. We measured response times as primary performance indicator, pupil size and HRV as physiological measures, and mental fatigue, task engagement, and perceived effort as subjective ratings. Both increased pupil size and increased HRV significantly predicted better task performance. However, the underlying associations between physiological measures and performance were influenced by task demands and time on task. Pupil size, but not HRV, results were consistent with subjective ratings. The empirical findings suggest that, by capturing variance in operators' mental effort, physiological measures, specifically pupil size, can contribute to the prediction of task performance. Their predictive value is limited by confounding effects that alter the amount of effort required to achieve a given level of performance. The outlined conceptual approach and empirical results can guide study designs and performance prediction models that examine physiological measures as the basis for dynamic operator assistance.

Exploring the influence of music on cognitive performance in female assembly line workers at a medical device manufacturing unit.

The significance of enhancing working conditions for the physical health and performance of workers, particularly female workers, underscores the need for research in this domain and the examination of interventions such as music. Previous studies have yielded diverse outcomes regarding the influence of music on individuals' performance; hence, further research in this area appears imperative. The aim of this study is to explore the impact of music on the cognitive and task performance of female assembly operation operators. This study is an interventional (quasi-experimental) study that involved 81 participants from the female workforce of the medical equipment assembly unit in Isfahan, Iran. The evaluation encompassed task performance, working memory using N-Back test, sustained attention using continues performance test (CPT), degree of sleepiness, and mental fatigue using Flicker Fusion test, along with physiological parameters such as heart rate and blood oxygen level. Participants underwent testing both in the presence of classical music and in a condition without music playback. The provision of background music enhanced the workers' sustained attention and working memory. It led to improved task performance and a reduction in drowsiness. Concerning physiological parameters, it resulted in a slight decrease in heart rate at the end of the work shift and a marginal increase in participants' blood oxygen levels. Background music enhanced working memory (p-value = 0.001), sustained attention (p-value = 0.001), and improved the task performance of workers(p-value = 0.005). Additionally, likely due to increased relaxation, it led to a decrease in heart rate (p-value = 0.001) and an increase in blood oxygen levels (p-value = 0.016). Music also played a role in reducing participants' sleepiness (p-value = 0.001).

Driving Forces of Strategic Outsourcing in Telecommunication Companies in Tanzania

Abstract This article aims to evaluate the driving forces of outsourcing strategies on the organizational performance of mobile network operators in mainland Tanzania. The objectives are to identify the driving forces of outsourcing and determine the challenges of outsourcing in telecommunication companies. The article adopted a descriptive cross-sectional survey. The selection of mobile companies is based on high market share and operation period. Findings show that customer needs for high-quality services, high-rated change in service, reduction of operational expenditure, and accessing the world’s capability are among the major driving forces for outsourcing. Dependability, loss of security, hidden costs, and selection of competent partners to mention a few are the challenges faced by telecom companies. It is noted that outsourcing enables network operators to advance their organizational performance in terms of operating cost, productivity, profitability, and market share. However, customer satisfaction suffers due to the inconsistency of the quality of service offered. Therefore, it is advised that mobile operators closely monitor outsourcing service providers to ensure that agreed service levels are met, thereby, safeguarding the reputation of the company to customers.

Usulan Perbaikan Tata Letak Kerja Proses Produksi Lempengan Paku Bumi menggunakan simulasi Promodel (Studi Kasus: PT GCE)

Increasingly tight competition in the business world encourages organizations to improve the quality of their output. PT GCE, a manufacturing company in the Sheet Metal industry, with output based on employment. In the manufacturing process various obstacles often arise which result in delays in the processing time. To address these issues, they use computer simulations, primarily ProModel, to identify and overcome bottlenecks in resource utilization, production capacity, and inventory levels. This research focuses on PT GCE and queuing problems caused by several production processes, with the author analyzing the use of ProModel to improve production systems and increase performance. As a result of this research, the author made improvements to the production process system in order to increase performance, which can be done by increasing the number of employees or operators on machines where there is a buildup of goods, combining the Welding and Fit Up processes in order to optimize production time and areas as well as bringing work stations closer together with related processes and optimize operator performance so there is no waiting time. Keywords: Computer Simulation, Factory Layout, ProModel

Neural dynamical operator: Continuous spatial-temporal model with gradient-based and derivative-free optimization methods

Data-driven modeling techniques have been explored in the spatial-temporal modeling of complex dynamical systems for many engineering applications. However, a systematic approach is still lacking to leverage the information from different types of data, e.g., with different spatial and temporal resolutions, and the combined use of short-term trajectories and long-term statistics. In this work, we build on the recent progress of neural operator and present a data-driven modeling framework called neural dynamical operator that is continuous in both space and time. A key feature of the neural dynamical operator is the resolution-invariance with respect to both spatial and temporal discretizations, without demanding abundant training data in different temporal resolutions. To improve the long-term performance of the calibrated model, we further propose a hybrid optimization scheme that leverages both gradient-based and derivative-free optimization methods and efficiently trains on both short-term time series and long-term statistics. We investigate the performance of the neural dynamical operator with three numerical examples, including the viscous Burgers' equation, the Navier–Stokes equations, and the Kuramoto–Sivashinsky equation. The results confirm the resolution-invariance of the proposed modeling framework and also demonstrate stable long-term simulations with only short-term time series data. In addition, we show that the proposed model can better predict long-term statistics via the hybrid optimization scheme with a combined use of short-term and long-term data.

Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety.

Measurement of sound perception to improve the performance of military ships

The performance of military ships relies on the performance of equipment and operators. Acoustic performance of military ships consists of: - the acoustic discretion of the vessel, allowing a strategical advantage in the event of a threat (more specifically: noise and vibrations management of ship sources); - the acoustic comfort of the operators in the ship, allowing better oral communication, better perception of safety alarms and a better focus. These effects can reduce time loss, and prevent operators from making errors. It is therefore important to manage the acoustic environment on board throughout the design process, in order to ensure both the acoustic discretion of the ship and the performance of its operators on board. For several years, Naval Group is interested in optimizing the performance of the operators, coupling both acoustic and visual perceptions through virtual reality (VR). This congress is an opportunity to expose recent works on acoustic perception in a ship. Our results are based on both numerical simulations and experimental measurements.

Generic and site-specific social life cycle assessment of municipal wastewater treatment systems in Spain: challenges and limitations of the method when applied to resource recovery systems

Abstract Purpose This work aims to provide insights on the application of social life cycle assessment (S-LCA) in evaluating the social impacts associated with municipal wastewater treatment (WWT). The study assesses the social risks and social performance of two municipal WWT systems in Catalonia, Spain: a conventional wastewater treatment plant (WWTP) (Reference System) and a novel system that recovers water and other valuable resources (Novel System). Methods S-LCA was conducted at Generic and Site-Specific levels using 1 m3 of wastewater treatment as the functional unit (FU). The Generic assessment was conducted via the Product Social Impact Life Cycle Assessment (PSILCA) database, while the Site-Specific assessment employed the Subcategory Assessment Method (SAM) with four-level reference scales to assess the social performance of the WWTP operator and its first-tier suppliers. Furthermore, activity variables were calculated based on organizations’ shares in the total costs per FU, and the Novel System’s multifunctionality was solved through economic allocation. Results were aggregated by (i) assigning equal weights to organizations and (ii) factoring in organizations’ weights and the allocation factor, leading to results per FU. Results and discussion The Generic analysis results indicated that the Novel System entailed fewer social risks than the Reference System. Most social risks in both systems occurred in the subcategories “Access to material resources,” “Fair salary,” “Freedom of association and collective bargaining,” “Contribution to economic development,” and “Corruption.” In the Site-Specific assessment, the Novel System presented better social performance than the Reference System per 1 m3 of wastewater treatment. The latter’s performance per FU did not meet the basic requirement in four out of eleven subcategories, mainly due to the performance and weight of a chemical supplier. Allocation greatly benefitted the Novel System’s results per FU compared to the results obtained when equal weights were applied. Conclusions Activity variables were used to connect organizations’ conduct with particular WWT systems, and multifunctionality was solved. This approach allowed for obtaining results per FU at both assessment levels. However, social performance was also evaluated by calculating the average social performance of each system without considering activity variables and the FU, leading to different results. The social performance of the Novel System per FU was satisfactory across all subcategories but required improvement in four subcategories based on the average results. Given the limitations of using activity variables and allocation in S-LCA, further research is necessary to appropriately evaluate and compare the social effects of novel resource recovery systems.