Operator Workload Research Articles

BiloKey : A Scalable Bi-Index Locality-Aware In-Memory Key-Value Store

Fast in-memory key value stores are the keys to building large-scale Internet services. The state-of-the-art solutions mainly focus on optimizing the performance for read-intensive workloads. Nevertheless, a wide range of applications demonstrate a significant amount of updates and range queries, which scale poorly with the current implementations. In this paper, we present BiloKey, a highly scalable in-memory key value store on multi-core machines, significantly outperforming Redis and Memcached for a variety of mixed read and write workloads. To achieve this, BiloKey leverages a fast bi-index comprised by a Hash Table index and a SkipList index, where the former supports feature rich operations including GET, UPDATE and DELETE with $O$O(1) complexity, while the latter supports SCAN with $O$O(log$N$N) complexity. Furthermore, to make the bi-index design scale well, BiloKey adopts three techniques: lazy synchronization for reducing the overhead of maintaining index consistency, lock-free data structure for supporting multi-writers, and locality-aware data parallel processing for preserving the data locality of requests. Compared with two popular in-memory KV stores (i.e., Redis and Memcached), experimental results show that: (1) for write-intensive workloads, BiloKey outperforms Redis and Memcached by 7.8x and 3.7x on average (up to 11.5x and 4.8x), respectively; (2) for scan-intensive workloads, BiloKey achieves an average speedup of 2.3x against Redis; (3) for read-intensive workloads, BiloKey also outperforms Redis and Memcached by 1.2x and 1.8x on average.

Investigating Ensemble Learning and Classifier Generalization in a Hybrid, Passive Brain-Computer Interface for Assessing Cognitive Workload.

Hybrid, passive brain-computer (h/pBCI) interfaces have received much attention in regards to measuring various mental states. A high classification rate of operator workload state is necessary in order to be able to enhance operator performance. Physiological measures have been used with machine learning algorithms to classify workload state, however, these measures are hypothesized to suffer from inherent nonstationarity. To attain a more generalizable classifier, a prior solution has been to use a multi-day learning paradigm to train classifier models. In earlier work, we have shown that increasing the number of unique data sessions used to form a learning set can improve the accuracy of classifying mental workload where improved generalizability is partly attributable to the multi-day paradigm. To further investigate methods that produce more generalizable classifiers, we look to ensemble learning. Here we implement ensemble learning to increase accuracies, reduce variance, and leverage theoretical performance of the ensemble as compared to observed to make inference about generalization. An adaptive boosting method (AdaBoost) is used to train a "base learning algorithm" multiple times, adaptively adjusting to errors and forming a vote out of the resulting hypotheses using three different base learning algorithms: an artificial neural network (ANN), a support vector machine (SVM), and linear discriminant analysis (LDA). We observed that the ensemble converged on theoretical performance with respect to error and variance only when the training sets were formed using the multi-day paradigm. These results indicate that ensemble learning approaches can be used in examples of pBCI such as those designed here, but they are also affected by theorized nonstationarity in physiological response. The observation of ensemble convergence on theoretical performance may be used to make inference about generalizability when simple accuracy of detection can be misleading.

Burnout Among Business Students and Its Impact On Academic Performance

Purpose: Purpose of this research is to check the impact of Burnout among with Work Load and Stress on the Academic Performance of the university and thus then the ways to cope up with the burnout and then to get maximum academic performance.
 Design/Methodology/Approach: Total of 750 students were taken as population of this research and sample of 250 students were taken and distributed the questionnaire. The responses were get back from 150 students to analyze through the SPSS.
 Findings: This study find out there exist a significant strong inverse relationship among Work Load, Stress, Burnout and Academic Performance. Like Academic performance will gets decrease if Burnout, Work Load and Stress will be more.
 Limitations: The sample size chosen for this research is very small and the time taken for this study was very limited.

ANALISIS BEBAN KERJA DAN JUMLAH OPERATOR PENULANGAN RANGKA BETON UDIT

This study was conducted at a factory producing construction and building materials such as ready mix concrete, piles, udit concrete, and ryol. These products are produced in almost the same process, but on different production lines. There are 2 operators in the frame reinforcement section for udit concrete production. It is seen that the two operators have the same task but different results of their work, so that the production target is not achieved. The workload analysis method is used to determine the operator's actual load. Observation of activities is carried out by the work sampling method for 5 days. In work sampling, work and idle activities of workers are observed at randomly selected times and the amount of production during observations is recorded. From the study, it is known the ideal of actual load of each operator and the number of operators for the concrete frames reinforcement. The results showed workload of operator 1 was 95.11% while workload of operator 2 was 107%. The number of operators reinforcing concrete frames remains fixed for 2 people, but wage compensation needs to be given to operators 2 for excess workload refers to the amount of wages and provisions for overtime that are applicable in the company.

Predicting Locomotive Crew Performance in Rail Operations with Human and Automation Assistance

As new technologies are introduced into rail operations, models are needed to represent the task load of operators to identify periods of extreme workload that could be mitigated through technological interventions. To this end, a computational model is described to quantitatively simulate freight rail operator workload to understand the impacts of inserting intelligent automation on different crew configurations. A detailed task analysis served as the basis for identifying tasks performed during transit. Utilizing task characteristics and operating conditions as inputs, a discrete event simulation was designed to predict human operator workload. Results show that during heavy-traffic conditions, the presence of automation can impact the locomotive engineer performance more than the presence of a freight conductor in a short-haul freight rail setting. However, under typical conditions, assistance may not be as beneficial for human operator performance.

A Novel Robot-Assisted Endovascular Catheterization System With Haptic Force Feedback

The robot-assisted endovascular catheterization system (RAECS) has the potential to address some of the procedural challenges and separate interventionalists from the X-ray radiation during the endovascular surgery. However, the employment of robotic systems is partly changing the natural gestures and behavior of medical professionals. This paper presents a RAECS that augments surgeon's motions using the conventional catheter as well as generates the haptic force feedback to ensure surgery safety. The magnetorheological fluids based haptic interfaceexperiment is proposed to measure the actions of the operator and provide the haptic force. The slave catheter manipulator is presented to actuate the patient catheter in two degrees of freedom (radial and axial direction). In addition, a force model is provided to characterize the kinematics of the catheter intervention. Afterward, the “pseudocollision” and “real collision” are utilized to descript the catheter tip–vessel interaction. The in vitro results are presented, which demonstrate that catheter-based haptic force feedback has a benefit for improving catheterization skills as well as reducing the cognitive workload of operators.

Analisis Beban Kerja Mental Pada Pekerja Di Industri Pembuatan Tempe

Perusahaan X merupakan UMKM produsen tempe, aktivitas yang diteliti dalam proses produksi tempe yaitu aktivitas pencucian kedelai, pengangkatan keranjang dan pengemasan. Tujuan dari penelitian ini yaitu mengetahui beban kerja mental pada operator yang melakukan aktivitas produksi berupa pencucian kedelai, pengangkatan keranjang dan pengemasan. Penelitian ini menggunakan metode NASA-TLX dengan indikator berupa mental demand (MD), physical demand (PD), temporal demand (TD), performance (P), effort (E), dan frustration level (FR). Hasil pengolahan data menunjukkan bahwa aktivitas pencucian kedelai memiliki skor NASA-TLX sebesar 88, aktivitas pengangkatan keranjang memiliki skor sebesar 88,67, dan aktivitas pengemasan memiliki skor sebesar 90. Hasil skor tersebut menunjukkan bahwa beban kerja pada aktivitas pencucian kedelai, pengangkatan keranjang, dan pengemasan tergolong sangat tinggi untuk operator. Dari hasil tersebut, maka perlu dilakukan perbaikan sistem kerja agar beban kerja mental operator dapat berkurang sehingga meningkatkan produktivitas dan kesehatan operator.

A Robot Learning Method with Physiological Interface for Teleoperation Systems

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environments, the manipulator’s performance and efficiency of the human-robot interaction in the tasks may degrade significantly. In this study, a novel method of human-centric interaction, through a physiological interface was presented to capture the information details of the remote operation environments. Simultaneously, in order to relieve workload of the human operator and to improve efficiency of the teleoperation system, an updated regression method was proposed to build up a nonlinear model of demonstration for the prescribed task. Considering that the demonstration data were of various lengths, dynamic time warping algorithm was employed first to synchronize the data over time before proceeding with other steps. The novelty of this method lies in the fact that both the task-specific information and the muscle parameters from the human operator have been taken into account in a single task; therefore, a more natural and safer interaction between the human and the robot could be achieved. The feasibility of the proposed method was demonstrated by experimental results.

Analysis of Boiler Operation Workload in Salted Fish Small Medium Enterprises (SMEs)

A person’s physical condition at work is crucial to the production output. This can be influenced by operator fatigue factor based on operator’s calorie requirement while working, since the amount of energy that comes out is greater than the incoming energy at work. The operators working on fish boiling stations are 2 operators and with 2-3 hours working time per day. Thus, to determine the fatigue factor of the operator is to know the pulse operator to determine the number of calories needed by the operator while working. Then the determination of the classification of workload based on the increase in the maximum pulse due to the burden (cardiovascular load = CVL) in every activity undertaken by the worker needs to be known. It is expected that workers can do their work to the maximum in a long time. The method of Cardiovascular Load (CVL) is a calculation to determine the classification of workload based on the calculation of the working pulse rate difference with the resting pulse compared to the difference between the maximum pulse with resting pulse.

Efficient and Scalable Execution of Fine-Grained Dynamic Linear Pipelines

We present Pipelite , a dynamic scheduler that exploits the properties of dynamic linear pipelines to achieve high performance for fine-grained workloads. The flexibility of Pipelite allows the stages and their data dependences to be determined at runtime. Pipelite unifies communication, scheduling, and synchronization algorithms with suitable data structures. This unified design introduces the local suspension mechanism and a wait-free enqueue operation, which allow efficient dynamic scheduling. The evaluation on a 44-core machine, using programs from three widely used benchmark suites, shows that Pipelite implies low overhead and significantly outperforms the state of the art in terms of speedup, scalability, and memory usage.

LB-Grid: An SSD efficient Grid File

Recent advances in non-volatile memory technology have led to the introduction of solid state drives (SSD). NVMe SSDs are the latest development in flash based solid state drives and they were designed as a means of low latency and high bandwidth. Many research studies seek for taking advantage of this new technology to accelerate data management. Multidimensional indexes are fundamental for the efficiency of spatial query processing. In this work, we study the implication of high performance NVMe drives on spatial indexing. More specifically, we present an in-depth performance analysis of the Grid File in flash storage and we introduce LB-Grid, a write efficient variant of Grid File for flash based solid state drives. We present new query algorithms for both LB-Grid and Grid File that exploit the high internal parallelism and I/O bandwidth of NVMe SSDs. Experimental results unveil the efficiency of the proposed algorithms. Utilizing a test set of 500M points, LB-Grid appears to be up to 2.26 times faster than Grid File, up to 5.5 times faster than the R∗-tree, and up to 3.3 times faster than the FAST-Rtree in update intensive workloads. On the other hand, the Grid File presents better performance in read intensive workloads; exploiting a batch read operation, it achieves a speedup up to 10.2x in range queries, up to 1.56x in kNN and 4.6x in group point queries.

Two-channel in-ear EEG system for detection of visuomotor tracking state: A preliminary study

Evaluating an operator's mental workload during work activities is crucial to maintain safety and performance. By minimizing human error associated with work demands, especially in a hazardous environment, potentially serious errors may be avoided. This study aims to assess the feasibility of using an in-ear EEG system to classify the user's state in a visuomotor tracking task that may influence mental workload and motor action. A two-channel wireless in-ear EEG system was used to record EEG signals while subjects performed the task using a joystick to manipulate an object displayed on a monitor. A highly comparative time series analysis was employed on the processed signals to extract and select the top features for each subject individually. The features sets were trained and tested with support vector machines, random forest, linear discriminant analysis, subspace discriminant, and neural network to compare their performances. Models trained on two trials, each 14 minutes in duration and tested on the other trial were able to yield an accuracy of 79.30 ± 4.85% on average across the ten participants with an individualized moving average threshold filter and classifier. This proof-of-concept study demonstrates the feasibility of using a two-channel wireless in-ear EEG system as a viable solutions to develop wearable devices to detect mental workload associated with the execution of visuomotor tasks.

How good is the OpenPOWER architecture for high-performance CPU-oriented weather forecasting applications?

Performance, i.e., execution times, is one of the most important features of HPC software, but energy consumption is also growing in importance if we intend to extend application to Exascale. This is the case of HPC software used in weather forecasting, in which every ounce of performance is critical in order to increase the accuracy and precision of its results. In this work, we study the performance-energy balance of an OpenPOWER processor, which is designed for the high workloads typically seen on data servers and HPC environments. Our results show that the OpenPOWER processor is superior in performance in weather forecast workloads compared to other processors commonly used in HPC, but at the expense of consuming more energy. Furthermore, the highest hyperthreading modes available on OpenPOWER processors do not perform well with HPC workloads and are even detrimental to performance.

Object-Centered Teleoperation of Mobile Manipulators With Remote Center of Motion Constraint

In the context of mobile manipulator teleoperation, intuitive control interfaces are fundamental not only to reduce the operator's workload, but also to improve the situational awareness during the task execution. In the case of object exploration tasks, different operator control units have been proposed, many of them inspired by the augmented reality, virtual reality, and computer games contexts. In this work, one such control interface, known as the orbit control mode , is formalized in terms of the remote center of motion constraints. This interface allows for the definition of a novel control mechanism able to take additional constraints, such as obstacle avoidance and manipulation dexterity, by the exploitation of the system redundancies. Experimental results obtained from a real teleoperation task confirm the validity of the approach.

Incorporating Trust and Self-Confidence Analysis in the Guidance and Control of (Semi)Autonomous Mobile Robotic Systems

We propose a trust and self-confidence-based autonomy allocation strategy to automatically choose between manual and autonomous control of (semi)autonomous mobile robots in guidance and navigation tasks. We utilize a performance-centric, computational trust and self-confidence model and automated autonomy allocation strategy, developed in our earlier work (H. Saeidi and Y. Wang, “Trust and self-confidence based autonomy allocation for robotic systems,” in Proc. 54th IEEE Conf. Decis. Control , 2015, pp. 6052–6057.), based on objective and unbiased performance measures for the human and the robot. A set of robot simulations with a human-in-the-loop is conducted for a teleoperated unmanned aerial vehicle tracking task. The results demonstrate that our allocation strategy can capture human autonomy allocation pattern with an accuracy of 64.05%. We also show that the strategy can improve the overall robot performance by 11.76% and reduce operator's workload by 10.07% compared to a manual allocation. Moreover, compared to a performance maximization strategy, our strategy is 23.42% more likely to be accepted and generally preferred and trusted by the participants. Furthermore, we design a decision pattern correction algorithm based on nonlinear model predictive control to help a human operator gradually adapt to a modified allocation pattern for improved overall performance.

Functional schemes of automated and robotic control of equipment in longwall top coal caving

The article proposes functional schemes of automated and robotic control of equipment in longwall top coal caving method (LTCC) with coal discharge to face conveyor. The application of the proposed schemes lessens the influence of the human factor, prevents coal dilution, eliminates conveyor overloading, decreases workload of shearer operator in face area and reduces the probability of making errors by operators.

Track-Before-Detect Bearings-Only Localization Performance in Complex Passive Sonar Scenarios: A Case Study

Track-before-detect (TkBD) algorithms have been shown to greatly abate measurement-to-track association (MTA) challenges. These simplifications are aptly relevant for reducing operator workload in deployed sonar systems that require a human “in the loop.” This paper presents a case study of a passive bearings-only target motion analysis TkBD algorithm using two complex data sets: one simulated and one obtained at sea, which are representative of scenario characteristics typically encountered with advanced operational sonar systems. The simulated data set exhibits crossing contacts of differing signal-to-noise ratios and short data gaps. The at-sea data, obtained from an open ocean exercise, are used to assess performance under realistic conditions that exhibit high levels of clutter, varying background noise levels, fading contacts, a high bearing-rate contact, and large data gaps. The TkBD algorithm is designed for the general complexities encountered in the examined data sets and employs a particle filter that defines its likelihood function as the accumulation of raster bin values from the output of the sonar beamformer conditioned on the particle's state trajectory. For the scenarios examined the localization performance of the algorithm is encouraging. The paper briefly reviews the chief motivation for using a TkBD approach, which is to circumvent MTA.

Balancing high operator's workload through a new job rotation approach: Application to an automotive assembly line

Reducing musculoskeletal disorder (MSD) risk factors and work monotony are the main reasons that persuade a manufacturing industry to apply job rotation in the workforce planning. This research aims to smooth the daily workload by designing an optimal sequence of job rotation using a mathematical model. The study consists of two main steps. The first is an ergonomic analysis of the workstations to evaluate the physical workload of different jobs. An in-house risk assessment method was used to identify physical workloads of each job. In the second step, a mathematical model was developed to schedule job rotation and optimize/balance the cumulative workload. Mixed integer programming is proposed to implement a platform for the ergonomic job rotation.The objective of the job rotation programming in this research was to determine the optimal sequence of jobs for each worker such that the global daily workload of the workers would be balanced. The proposed job rotation strategy reduced the dispersion and the deviation of the daily cumulative workload among the workers considering that the rotation only occurred inside the specific groups and not between the groups.

A mixed integer programming (MIP) model for evaluating navigation and task planning of human–robot interactions (HRI)

Exercise of robotics in many applications brings in concerns of human–robot interaction. This paper offers a mathematical model-based mission planning tool for optimizing operator workload and platform utilization in human/multi-robot (H/M-R) teams. None of the earlier methods consistently predicts fan-out (number and configuration of robots that can be operated simultaneously and effectively, a critical H/M-R design decision). In this research, a mixed integer programming (MIP) model and solution framework are proposed to provide better estimates of fan-out while explicitly considering the performance, mission characteristics, objective and task/environment complexity. The extent of each robot’s waiting time is restricted by a utilization threshold in the MIP model. The effect of environment’s complexity on the task effectiveness is considered, where robots’ performances deteriorate during switch and neglect times. Simulation results show that fan-out effect is dependent on interaction efficiency, neglect tolerance, as well as other parameters. Performance is most sensitive to environment’s complexity and least sensitive to utilization threshold. In addition, the MIP model reveals optimal control sequence of robots to prevent switching confusions and maximize team performance. Empirical evaluations show that this approach holds great promise for real-world scenarios.

Improving the Performance and Energy Efficiency of GPGPU Computing through Integrated Adaptive Cache Management

Hardware caches are widely employed in GPGPUs to achieve higher performance and energy efficiency. Incorporating hardware caches in GPGPUs, however, does not immediately guarantee enhanced performance and energy efficiency due to high cache contention and thrashing. To address the inefficiency of GPGPU caches, various adaptive techniques (e.g., warp limiting) have been proposed. However, relatively little work has been done in the context of creating an architectural framework that tightly integrates adaptive cache management techniques and investigating their effectiveness and interaction. To bridge this gap, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. IACM integrates the state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1 and 61.9 percent on average, respectively), achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4 percent at maximum and 7.7 percent on average), and delivers significant performance and energy-efficiency gains over the baseline GPGPU architecture enhanced with advanced architectural technologies.