A Novel Training and Collaboration Integrated Framework for Human–Agent Teleoperation

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.

Hard Backreaming Due to Hole Shrinkage Through Carbonate Reservoir in Offshore Abu Dhabi

The 3,000 ft long lateral holes drilled through water-injected area in the carbonate reservoir in the offshore Abu Dhabi have been forced to implement hard backreaming. The abnormal extra operational time has been taken due to poor performance in the operation to pull out a bottomhole assembly after drilling to the total depth. The study aims to analyze root-causes of the hard backreaming through the carbonate reservoir in the studied field. The speed of tripping-out was analyzed every stand of drill pipe by using time domain data of movement of traveling block. The correlations between the speed of tripping-out and rock characteristics such as porosity and constituent minerals in rocks were investigated. Hole shape was analyzed in the representative intervals of low trip-out speed using 16-sector caliper derived from azimuthal density logging. Stress concentration around the borehole wall was also analyzed using geomechanical model. The investigation revealed that hole shrinkage due to plastic deformation of the borehole wall was the most possible root-cause of the hard backreaming in the carbonate reservoir. Namely, BHA had to ream up deformed borehole wall in tripping-out. From the viewpoint of rock characteristics, the speed of tripping-out was found to be lower in the specific geologic layers with higher content of dolomite. This is because dolomite rocks cause larger resistance in reaming it in tripping-out since the strength of dolomite rocks is larger than that of limestone. Based on our findings, use of reamers on bit is found to be the better solution to improve the tripping-out performance in the problematic geologic layers instead of conventional operational attempts such as spotting of acid and use of high viscous fluids in hole cleaning. In addition, optimization of the design and position of reamers and stabilizers is essential to succeed in the future 10,000 ft long extended-reach wells in the studied oil field.

Effect of operational time on the chemical oxygen demand performance of sequencing batch reactor treating disperse dye synthetic wastewater

This work examines the effect of operational time of 6 hours on the removal of disperse dye from synthetic textile wastewater. Experiments were conducted daily at fill, react, settle, draw, and idle phase at 1 h, 1 h, 2 h, 1 h, 1 h respectively. The results showed that the highest removal efficiency of COD reached 77 %. Short operational time resulted in low COD removal efficiencies of disperse dye. The findings also revealed that when applying optimum operational time, sequencing batch reactor will achieve the highest growth of the bacteria responsible for the degradation of COD. When operational time increases, degradation becomes the dominant removal mechanisms of COD.

Cancellation of Elective Surgeries in A Tertiary Care Hospital in North India

Background: Cancellation of elective surgical treatments is a quality-of-care issue as well as a huge waste of health-care resources. Patients may experience emotional distress as a result of this, as well as difficulty for their families. Aim: To find the significant reasons of cancellation of scheduled surgical cases. Methods: A total of 300 elective operations in our institution were chosen. The completed surgeries were planned on the scheduled operation day, and the anaesthesiologist noted down a list of cancellations along with their reasons. Results: A total of 300 patients were scheduled for surgery. A total of 60 patients were cancelled, resulting in a 20% cancellation rate. Lack of operational time was the most prevalent reason for cancellation. Conclusion: The majority of the reasons for cancellation should have been avoided with proper list preparation and the surgical team's meticulous planning.

Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains

The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps.

A note on ’Variable neighborhood search based algorithms for crossdock truck assignment’

Some implementations of variable neighborhood search based algorithms were presented in C´ecilia Daquin, Hamid Allaoui, Gilles Goncalves and Tient´e Hsu, Variable neighborhood search based algorithms for crossdock truck assignment, RAIRO-Oper. Res., 55 (2021) 2291-2323. This work is based on model in Zhaowei Miao, Andrew Lim, Hong Ma, Truck dock assignment problem with operational time constraint within crossdocks, European Journal of Operational Research 192 (1), 2009, 105-115 (see Miao et al. (2009)) which has been proven to be incorrect. We reiterate and elaborate on the deficiencies in the latter and show that the authors in the former were already aware of the deficiencies in the latter and the proposed minor amendment does not overcome any of such deficiencies.

Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes

With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

Multi-criteria optimization of a PCM based heat sink - Effect of geometric parameters on operational time and melt fraction

Computational fluid dynamic analysis of a PCM (Phase Change Material) based heat sink has been carried out in the present study. The PCM used is RT44HC. Longitudinal fins made of aluminum have been considered. The influence of pertinent parameters viz. fin number, fin thickness, orientation and base thickness on melt fraction and operational time have been analyzed. The critical temperature considered for the study is 54.8°C. The melting behavior of the PCM is simulated by employing the Volume of Fluid (VOF) method. The design of the experiment has been performed using the Taguchi method. By employing grey relational multi-criteria optimization technique and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method the best thermally performing configuration has been attained through the optimum values of operational time and melt fraction. In addition to the above ANOVA (Analysis of Variance) is performed to find the most significant parameter. Based on the investigation fin thickness and number of fins are observed to significantly influence the thermal transport.