Process Discovery and Refinement of an Enterprise Management System

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data. This data can be extremely valuable for executing organizations because the data allows constant monitoring, analyzing, and improving the underlying processes, which leads to the reduction of cost and the improvement of the quality. Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours. This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints. By investigating and analyzing their order handling processes, this study aims at learning a model that gives insight inspection of the processes and performance analysis. Furthermore, the animation is also performed for the better inspection, diagnostics, and compliance-related questions to specify the system. The configuration of the system and the conformance checking for further enhancement is also addressed in this research. To achieve the objectives, this research uses process mining techniques, i.e. process discovery in the form of formal Petri nets models with the help of process maps, and process refinement through conformance checking and enhancement. Initially, the identified executed process is reconstructed by using the process discovery techniques. Following the reconstruction, we perform a deep analysis for the underlying process to ensure the process improvement and redesigning. Finally, some recommendations are made to improve the enterprise management system processes.     

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.     

A robust and efficient convolutional deep learning framework for age-invariant face recognition

Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age-invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age-invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state-of-the-art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research.     

A Knowledge Based Design Methodology for manufacturing assembly lines

In assembly line design, the problem of balancing has received most attention from past researchers, and a number of algorithms have been devised for the analysis of single, multi- and mixed-product assembly lines [Int. J. Prod. Res. 27 (1989) 637]. In many cases, such algorithms seek a solution for the particular situation, which is under consideration and therefore have very little flexibility for generic application to assembly line design. Real life practical design issues include stochastic operation times, parallel workstation requirements, feasibility for workstation combining, and parallel line implementations, all of which are features which are ignored in many analyses. This paper presents a Knowledge Based Design Methodology (KBDM) for automated and manual assembly lines, which can be applied equally well to single, multi- and mixed-product assembly lines with either deterministic operation times or stochastic operation times. The methodology starts from a suitable assembly system selection and thereafter decides suitable cycle times, parallel workstation requirements, and parallel line implementation for the type of assembly system being selected. An economical number of workstations are decided with the aid of workstation combining options depending upon the factual information provided. The end result is the detailed design of a manufacturing assembly line. A case study from a practical assembly line is presented to illustrate how the KBDM works.     

Behavioral adaptation of information systems through goal models

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.     

单相并联型有源滤波器的鲁棒电流控制方法

有源滤波器与无源滤波器相比有很多优点,是消除或限制电力系统谐波污染的一种有效措 施。电流环的控制是有源滤波器控制的关键部分。传统的并联型有源滤波器需要对多个电流进行 采样,文中提出一种仅采样电源电流的新方法。考虑到对瞬时电流控制时容易引入噪声,影响系统 的稳定性,文中在无差拍控制的基础上,提出了采用可复位积分器作为输入滤波器的平均电流控制 方法,该方法对参数的变化和采样噪声及周期噪声都有很强的鲁棒性。仿真和实验证实了该方法 的有效性。     

Stabilized active camera tracking system

We present a robust and real-time stabilized active camera tracking system (ACTS), which consists of three algorithmic modules: visual tracking, pan-tilt control, and digital video stabilization. We propose an efficient correlation-based framework for visual tracking module that is designed to handle the problems which severely deteriorate the performance of a traditional tracker. The problems that it handles are template drift, noise, object fading (obscuration), background clutter, intermittent occlusion, varying illumination in the scene, high computational complexity, and varying shape, scale, and velocity of the manoeuvring object during its motion. The pan-tilt control module is a predictive open-loop car-following control strategy, which moves the camera efficiently and smoothly so that the target being tracked is always at the center of the video frame. Video stabilization module is required to eliminate the vibration in the video, when the system is mounted on a vibratory platform such as truck, helicopter, ship, etc. We present a very efficient video stabilization method that adds no extra computational overhead to the overall system. It exploits the coordinates of the target, computed by the tracker module, to sense the amount of vibration and then filters it out of the video. The proposed system works at full frame rate (30 fps), and has been successfully used in uncontrolled real-world environment. Experimental results show the efficiency, precision, and robustness of the proposed stabilized ACTS.     

A nonlinear quantization scheme for two-layer HDR image encoding

Two-layer schemes provide an effective method of encoding high dynamic range images with backward compatibility. The first layer is the tone-mapped low dynamic range version of the original image, used for visualization. The residual information that cannot be preserved in the first layer is stored in the second layer, which itself is generally encoded as an image of a fixed bit-depth. Any further details that cannot be preserved in the second layer are discarded. In this paper, we present a nonlinear quantization algorithm that can significantly enhance the amount of details that can be preserved in the second layer, and therefore improve the encoding efficiency. The proposed technique can be incorporated in any existing two-layer encoding method and leads to significant improvement in their performance.     

Non-Intrusive Monitoring of Everyday Behavioral Activities in an Indoor Environment

A non-intrusive design for monitoring everyday activities of an elderly person is presented. The proposed system is intended to be used in the bedroom, allowing the elders to stay at home in a safe environment. The required hardware design is simple in structure and cost effective. The sensor design is implemented by using capacitive sensors and an Arduino microcontroller unit. And a real time graphical user interface is implemented to monitor the elderly person. The performance analysis shows that the sensor design is able to differentiate between a human body and a house pet.     

Theoretical investigation of plasma dynamical behavior and halo current analysis

In this paper, a novel system has been developed for plasma disruption conditions followed by downward vertical displacement. During the disruption, size and orientation of plasma decreases, which gives the halo current circulated around each contacting point in radial as well as in poloidal directions. Therefore, a new mathematical model has been developed, which gives the interaction forces of halo current, vertical, and radial plasma dynamical behavior (linear and nonlinear). This theoretical approach showed that the tokamak plasma has two connecting points in order to distinguish between the stable and unstable position. This model can particularly give the magnetic field change points and changing of flux, which are more convenient in order to discuss the static and tilting position of plasma behavior. Numerical techniques have been calculated in terms of plasma dynamical behavior, that is, Electromagnetic/plasma, Vertical Displacement Event (VDE) stages, and initial interaction between the forces under specific time interval. The objective of the research is to developed theoretical and computational model in order to investigate the dynamical behavior of plasma under disruption conditions. This is the novel method, and no work has been reported so far. Copyright © 2015 John Wiley & Sons, Ltd.