PROBABILITY DISTRIBUTION OF TIME DURATION OF MANUAL OPERATION IN THE PRODUCTION OF GLASS EYES

When precise planning of capacities and times of production is needed, there must be precise data for calculation. Not all operations have to have a normal time duration distribution. Counting with average values or use values from guessed distribution can lead to mistakes in actual production planning. This article aims to determine time probability distributions to manual operations. Tests of goodness of fit are used to search for more suitable distributions. This approach is presented in a case study of glass eyes manufacturing. Results show that there can be differences between the estimated normal distribution and another more suitable one. By using tests of goodness of fit to define the correct distribution, more precise production and capacity planning results can be achieved.

Smart Webcam Cover

Laptop webcams can be covertly activated by malware and law enforcement agencies. Consequently, 59% percent of Americans manually cover their webcams to avoid being surveilled. However, manual covers are prone to human error---through a survey with 200 users, we found that 61.5% occasionally forget to re-attach their cover after using their webcam. To address this problem, we developed Smart Webcam Cover (SWC): a thin film that covers the webcam (PDLC-overlay) by default until a user manually uncovers the webcam, and automatically covers the webcam when not in use. Through a two-phased design iteration process, we evaluated SWC with 20 webcam cover users through a remote study with a video prototype of SWC, compared to manual operation, and discussed factors that influence users' trust in the effectiveness of SWC and their perceptions of its utility.

A Clinical Evaluation Study of Cardiothoracic Ratio Measurement Using Artificial Intelligence

Background Artificial Intelligence, particularly the Deep Learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on Chest x-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice. Methods We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7,517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9,386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland-Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements. Results The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet+VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet+VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced operating time by almost ten-fold (1.07 ± 2.62 secs vs 10.6 ± 1.5 sec) compared to manual operation. Conclusion Due to its exceptional accuracy and speed, the AlbuNet+VGG-11 model could be clinically implemented to assist radiologists with CTR measurement.

Transforming Water Injection Process With Real Time Automation

For mature fields, water injection is one of the widely deployed techniques to ensure continuous oil recovery from the reservoir by maintaining the reservoir pressure, oil rim and pushing the oil from injection to production wells. Thus, it is critical to ensure a continuous and reliable operation of water injection to have consistent and sustainable rate. This paper demonstrates the new approach, utilizing automation and digital technology providing operational improvement and reduction in unplanned production deferment (UPD). One of the methods to effectively manage the water injection operation is via automation of injection process, especially since most of the water injection facilities still rely heavily on manual operation. First, a discussion on typical water injection technique is discussed. Challenges and sub-optimal operation of water injection processes within the company and industry are analysed. Then, the designing of a fully automated water injection system, such as equipment availability and constraints in matching and responding to well injection requirement are demonstrated. While an immediate adoption of process automation to mature assets may be faced with challenges such as system readiness, hardware availability, capital investment and mindset change, a step-by-step approach such as guided operation and semi-auto operation is explored as preparation prior to a full automation roll-out. With the shift from manual operation reliance to automation, the response time to process changes is improved leading to reduction in near-miss and trip cases, and minimum unplanned deferment.

A Marker-Less Monocular Vision Point Positioning Method for Industrial Manual Operation Environments

Vision-assisted technologies in industry such as Augmented Reality (AR) are increasingly popular. They require high positioning accuracy and robustness in industrial manual operation environments. However the narrow space and moving hands or tools may occlude or obscure local visual features of operation environments, affect the positioning accuracy and robustness of operating position. It may even cause misoperation of operators because of misguidance. This paper proposes a marker-less monocular vision point positioning method for vision-assisted manual operation in industrial environments. The proposed method can accurately and robustly locate the target point of operation using constraint minimization method even the target area has no corresponding visual features in the case of occlusion and improper illumination. The proposed method has three phases: intersection generation, intersection optimization and target point solving. In the intersection generation stage, a certain number intersections of epipolar lines are generated as candidate target points using fundamental matrices. Here the solving constraint is converted from point-to-line to point-to-points. In the intersection optimization stage, the intersections are optimized to two different sets through the iterative linear fitting and geometric mean absolute error methods. Here the solving constraint is converted from point-to-points to point-to-point sets. In the target point solving stage, the target point is solved as a constrained minimization problem based on the distribution constraint of the two intersection sets. Here the solving constraint is converted from point-to-point sets to point-to-point and the unique optimal solution is obtained as the target point. The experimental results show that this method has a better accuracy and robustness than the traditional homography matrix method for the practical industrial operation scenes.

EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling

Molecular simulations, including quantum mechanics (QM), molecular mechanics (MM), and multiscale QM/MM modeling, have been extensively applied to understand the mechanism of enzyme catalysis and to design new enzymes. However, molecular simulations typically require specialized, manual operation ranging from model construction to post-analysis to complete the entire life-cycle of enzyme modeling. The dependence on manual operation makes it challenging to simulate enzymes and enzyme variants in a high-throughput fashion. In this work, we developed a Python software, EnzyHTP, to automate molecular model construction, QM, MM, and QM/MM computation, and analyses of modeling data for enzyme simulations. To test the EnzyHTP, we used fluoroacetate dehalogenase (FAcD) as a model system and simulated the enzyme interior electrostatics for 100 FAcD mutants with a random single amino acid substitution. For each enzyme mutant, the workflow involves structural model construction, 1 ns molecular dynamics simulations, and quantum mechnical calculations in 100 MD-sampled snapshots. The entire simulation workflow for 100 mutants was completed in 7 hours with 10 GPUs and 160 CPUs. EnzyHTP is expected to improve the efficiency and reproducibility of computational enzyme, facilitate the fundamental understanding of catalytic origins across enzyme families, and accelerate the optimization of biocatalysts for non-native substrate transformation.

The Research on Operation Automation Model for Information Equipment

Privilege control is an important problem to be solved in the operation of the intranet network of military industry. Solidifying the operation process and replacing the operation personnel with machines to complete configuration changes can reduce the frequency of manual operation by privileged personnel, thus limiting the operation privilege and improving the security of intranet network. Aiming at the high security and high efficiency requirements of operation of intranet information equipment, this paper designs an operation automation mode. Aiming at the problem that it is difficult for tools to adapt to heterogeneous equipment, this paper studies the commands adaptive technology for heterogeneous equipment based on abstract atomic operation. In addition, the model and technologies are verified by a specific scene of equipment location change. The results show that the above model and technologies achieve the goal of ensuring the security of operation and improving the efficiency of operation.

An Automated Hydrodynamically Mediated Technique for Preparation of Calibration Solutions via Capillary Electrophoresis System as a Promising Alternative to Manual Pipetting

In this paper, a novel procedure for preparing calibration solutions for capillary electrophoresis (CE)-based quantitative analysis is proposed. Our approach, named the automated hydrodynamically mediated technique (AHMT), uses a capillary and a pressure system to deliver the expected amount of working solution and diluent directly to a sample vial. As a result, calibration solutions are prepared automatically inside the CE instrument, without any or with minimal manual operation. Two different modes were tested: forward and reverse, differing in the direction of hydrodynamic flow. The calibration curves obtained for a model mixture of analytes using AHMT were thorough compared to the standard procedure based on manual pipetting. The results were consistent, though the volume of obtained calibration solutions and the potential risk of random errors were significantly minimized by AHMT. Its effectiveness was further enhanced by the application of SCIEX® nanoVials, reducing the actual volume of calibration solutions down to 10 μL.