Fast Image Reconstruction Technique for Parallel Phase-Shifting Digital Holography

For incoherent and coherent digital holography, the parallel phase-shifting technique has been used to reduce the number of exposures required for the phase-shifting technique which eliminates zero-order diffraction and conjugates image components. Although the parallel phase-shifting technique can decrease the hologram recording time, the image interpolations require additional calculation time. In this study, we propose a technique that reduces the calculation time for image interpolations; this technique is based on the convolution theorem. We experimentally verified the proposed technique and compared it with the conventional technique. The proposed technique is more effective for more precise interpolation algorithms because the calculation time does not depend on the size of interpolation kernels.

Time-Fractional Phase Field Model of Electrochemical Impedance

In this paper, electrochemical impedance responses of subdiffusive phase transition materials are calculated and analyzed for one-dimensional cell with reflecting and absorbing boundary conditions. The description is based on the generalization of the diffusive Warburg impedance within the fractional phase field approach utilizing the time-fractional Cahn–Hilliard equation. The driving force in the model is the chemical potential of ions, that is described in terms of the phase field allowing us to avoid additional calculation of the activity coefficient. The derived impedance spectra are applied to describe the response of supercapacitors with polyaniline/carbon nanotube electrodes.

Penentuan Tingkat Kritikalitas Peralatan Pembangkit Dengan Metode Equipment Criticality Management Dalam Rangka Penentuan Prioritas Pemeliharaan

The criticality level of equipment used at PT PLN (Pesero) power plants at present is using the Maintenance Priority Index (MPI) method. The calculation for the criticality rating of MPI equipment uses 4 (four) types of calculations, namely SCR, OCR, ACR and AFPF. To add to the consideration in determining the priority of equipment maintenance, an additional calculation of the criticality level of PLTU Tarahan equipment is carried out using the Equipment Criticality Management method. The Equipment Criticality Management method has 4 (four) assessment perspectives, namely Production, Safety, Environment and Equipment Failure. Calculations that have been carried out on the top 100 (one hundred) equipment in the PLTU Tarahan SERP using the Equipment Criticality Management method, there are 85 (eight five) equipment that has “High” criticality and 15 (fifteen) equipment in the “Medium” criticality category. 15 (fifteen) equipment that has “Medium” criticality is equipment that has backup and part of common generating equipment.

Dynamic Warping Network for Semantic Video Segmentation

A major challenge for semantic video segmentation is how to exploit the spatiotemporal information and produce consistent results for a video sequence. Many previous works utilize the precomputed optical flow to warp the feature maps across adjacent frames. However, the imprecise optical flow and the warping operation without any learnable parameters may not achieve accurate feature warping and only bring a slight improvement. In this paper, we propose a novel framework named Dynamic Warping Network (DWNet) to adaptively warp the interframe features for improving the accuracy of warping-based models. Firstly, we design a flow refinement module (FRM) to optimize the precomputed optical flow. Then, we propose a flow-guided convolution (FG-Conv) to achieve the adaptive feature warping based on the refined optical flow. Furthermore, we introduce the temporal consistency loss including the feature consistency loss and prediction consistency loss to explicitly supervise the warped features instead of simple feature propagation and fusion, which guarantees the temporal consistency of video segmentation. Note that our DWNet adopts extra constraints to improve the temporal consistency in the training phase, while no additional calculation and postprocessing are required during inference. Extensive experiments show that our DWNet can achieve consistent improvement over various strong baselines and achieves state-of-the-art accuracy on the Cityscapes and CamVid benchmark datasets.

A Probabilistic and Deterministic based Defect Prediction through Defect Association Learning in Software Development

Software development is a multitasking activity by an individual or group of team. Every one activity engages diverse tasks and complication. To accomplish quality improvement, it is essential to make every activity task free of defects. But locating and correcting defects is more expensive and time-intense. In the past, many potential methods have been used to predict potential drawbacks in the program based on the theory of probability facts. Because the probability method applies a random variable and probability distributions to find a solution, the result is always in a possible range that can be true at some time or may also be wrong. Therefore, an additional calculation method coupled with the probability of making it more accurate and new in predicting the defect of the program. In this paper, we propose a Probabilistic and Deterministic based Defect Prediction (PD-DP) through Defect Association Learning (DAL). The PD-DP implements a Probability association method (PAM) and Deterministic association method (DAM) to predict the software defect accurately in software development. The experimental evaluation of the PP-DP in compare to existing prediction methods shows enhancement in prediction accuracy

Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information

Most construction managers use deterministic scheduling techniques to plan construction projects and estimate their duration. However, deterministic techniques are known to underestimate the project duration. Alternative methods, such as Stochastic Network Analysis, have rarely been adopted in practical contexts as they are commonly computer-intensive, require extensive historical information, have limited contextual/local validity and/or require skills most practitioners have not been trained for. In this paper, we propose some mathematical expressions to approximate the average and the standard deviation of a project duration from basic deterministic schedule information. The expressions’ performance is successfully tested in a 4100-network dataset with varied activity durations and activity durations variability. Calculations are quite straightforward and can be implemented manually. Furthermore, unlike the Project Evaluation and Review Technique (PERT), they allow drawing inferences about the probability of project duration in the presence of several critical and subcritical paths with minimal additional calculation.

Classification Algorithm Based on Nodes Similarity for MANETs

This article describes an algorithm of classification by similarity of nodes in a MANET (Clustering). To optimize a network performance without influencing others, we must act only on the cluster structure. Any additional calculation clutters more the system. To overcome this limitation, a strong classification method is needed. The purpose of classification algorithms is the search for an optimal partition. This optimum is obtained iteratively refining an initial pattern randomly selected by reallocating objects around mobile centers. In order to partition the nodes into clusters, we used this technique (iterative reallocation) from the well known k-means algorithm. The algorithm conception is based on the k-means method that we improved and adapted to make it suitable for mobile ad hoc networks. The algorithm is implemented on OLSR giving birth to a new routing protocol: OLSRKmeans.

A New Parameter in Vortex Identification and Visualization: Symmetry of Vortical Flow

The present investigation introduces a parameter to represent symmetry of a vortex. An important feature of a vortex to be identified in many fluid engineering fields is its stability derived from the flow kinematics, which is associated with the symmetry of its vortical flow. Although many vortex definitions and identification methods have been proposed in terms of various physical aspects of a vortex, such symmetry property or its identification is lacked. We focus on the flow geometry specified by the velocity gradient tensor, and show that the feature of its complex eigenvectors derives the symmetry of the vortical flow in the swirl plane. This property is invariant, and extracts the high symmetry vortex or vortical region irrespective of the intensity of the vortex. It does not require additional calculation except the complex eigenvectors, but it brings useful information of the flow state and skewness of vortices.

Adaptive Rate Control Algorithm for H.264/AVC Considering Scene Change

Scene change in H.264 video sequences has significant impact on the video communication quality. This paper presents a novel adaptive rate control algorithm with little additional calculation for H.264/AVC based on the scene change expression. According to the frame complexity quotiety, we define a scene change factor. It is used to allocate bits for each frame adaptively. Experimental results show that it can handle the scene change effectively. Our algorithm, in comparison to the JVT-G012 algorithm, reduces rate error and improves average peak signal-noise ratio with smaller deviation. It cannot only control bit rate accurately, but also get better video quality with the lower encoder buffer fullness to improve the quality of service.