International Journal of System Assurance Engineering and Management最新文献

Life cycle cost (LCC) analysis is an important tool for effective infrastructure management. It is an essential decision support methodology for selection, design, development, construction, maintenance and renewal of railway infrastructure system. Effective implementation of LCC analysis will assure cost-effective operation of railways from both investment and life-cycle perspectives. A major setback in the successful implementation of LCC analysis by infrastructure managers is the availability of relevant, reliable, and structured data. Different cost estimation methods and prediction models have been developed to deal with this challenge. However, there is a need to include condition degradation models as an integral part of LCC model to account for possible changes in the model variables. This article presents an approach for integrating degradation models with LCC model to study the impact of change in design speed on key decision criteria such as track possession time, service life of track system, and LCC. The methodology is applied to an ongoing railway investment project in Sweden to investigate and quantify the impact of design speed change from 250 to 320 km/h. The results of the studied degradation models show that the intended change in speed corresponds to correction factor values between 0.79 and 0.96. Using this correction factor to compensate for changes in design speed, the service life of ballasted track system is estimated to decrease by an average of 15%. Further, the expected value of LCC for the route under consideration will increase by 30%. The outcome of this study will be used to support the design and requirement specification of railway track system for the project under consideration. 

The present paper addresses the reliability modeling of a three-unit soft biscuit-making system. The system under consideration consists of three units, namely the mixer, depositor, and oven. Depositor and oven are connected through the same conveyor belt, so if there is a failure in either of them then another will be in a down state. On the other hand, the mixer works as a separate unit that provides feed to the depositor. However, the mixer can also be in a down state if the failures of either depositor or oven are not repaired within the stipulated time. Two repair personnel are appointed to handle the failures associated with the units. The system is assessed by employing the semi-Markov process and regenerative point technique. Additionally, relevant measures of system effectiveness are derived, accompanied by a comprehensive sensitivity analysis to assess the impact of various parameters on the system’s performance. Graphical representations are employed to visually analyze the influence of these parameters on the system’s overall efficiency.

Oral Cavity Squamous Cell Carcinoma (OCSCC) represents a common form of head and neck cancer originating from the mucosal lining of the oral cavity, often detected in advanced stages. Traditional detection methods rely on analyzing hematoxylin and eosin (H&E)-stained histopathological whole-slide images, which are time-consuming and require expert pathology skills. Hence, automated analysis is urgently needed to expedite diagnosis and improve patient outcomes. Deep learning, through automated feature extraction, offers a promising avenue for capturing high-level abstract features with greater accuracy than traditional methods. However, the imbalance in class distribution within datasets significantly affects the performance of deep learning models during training, necessitating specialized approaches. To address the issue, various methods have been proposed at both data and algorithmic levels. This study investigates strategies to mitigate class imbalance by employing a publicly available OCSCC imbalance dataset. We evaluated undersampling methods (Near Miss, Edited Nearest Neighbors) and oversampling techniques (SMOTE, Deep SMOTE, ADASYN) integrated with transfer learning across different imbalance ratios (0.1, 0.15, 0.20, 0.30). Our findings demonstrate the effectiveness of SMOTE in improving test performance, highlighting the efficacy of strategic oversampling combined with transfer learning in classifying imbalanced medical datasets. This enhances OCSCC diagnostic accuracy, streamlines clinical decisions, and reduces reliance on costly histopathological tests.

Ensuring the reliability of software is a critical task, particularly in the context of open-source projects. The complexity intensifies due to factors such as varying programmer skills, diverse testing environments, and different testing methodologies. This article emphasizes a significant challenge in software reliability—the influence of environmental factors throughout the software's life cycle. The proposed solution involves a novel Software Reliability Growth Model that considers time-dependent environmental factors, incorporating the change point phenomenon. To validate the model, real failure data from two Apache Software Foundation Projects, Log4j and Lucene, has been utilized, resulting in highly promising and encouraging outcomes.

After the pandemic, the whole world is transforming digital, due to the increased usage of handheld devices like smartphones and due to the evolution of the internet. All the transactions are becoming online. The security at end devices is an important issue to everyone. We believe that the data in transit is more secure, but in reality this is not true. The data are in the hands of bad actors for malicious activities. Android ransomware is one of the most widely distributed assaults throughout the world. It is a type of virus that prevents users from accessing the operating system and encrypts the essential data saved on their device. This work focuses on thorough assessment and detection of android ransomware application using machine learning methods. After a thorough analysis of existing mechanisms of android ransomware detection, we found that the combination of static behaviour with machine learning techniques can detect android ransomware with good accuracy. We have analysed 3572 samples of ransomware applications and 3628 samples of benign applications of various family. For classification, the decision tree, random forest, extra tree classifier, light gradient boosting machine methods are selected from the pool of classifier. The dataset was obtained from Kaggle, which is an open source dataset repository. The suggested model outperforms with a detection accuracy of 98.05%. Based on its best performance, we believe our suggested approach will be useful in ransomware and forensic investigation.

Efficient transportation of fruits and vegetables is crucial for proper storage, handling, and distribution directly influencing their quality, shelf life, and ultimately the price. Maintaining optimal storage conditions during the transport of fruits and vegetables is of utmost importance to preserve their freshness and quality. Therefore, there is a pressing need for a real-time assessment system that can ensure the highest quality and safety of fruits and vegetables throughout the supply chain network. This paper introduces an Internet of Things-enabled sensor network designed to address these challenges. The sensors are strategically deployed within the storage containers that continuously assessing real-time critical environmental parameters, such as temperature, humidity, pH, and air quality. These parameters significantly affect the storage of fruits and vegetables throughout the supply chain network. Furthermore, we have employed machine learning algorithms, such as decision trees, k-nearest neighbors, logistic regression, and Support Vector Machine, to measure performance in terms of accuracy, F1-score, precision, sensitivity, and specificity. The results indicate that the Support Vector Machine algorithm outperforms with the other algorithms with an impressive accuracy of 98.05%. Future research endeavors will focus on optimizing food supply chain loss.

The railway system is a complex technical system-of-systems (SoS). To address the complexity of the railway system, a holistic approach is needed that facilitates the development of an appropriate asset management regime. A systems-of-systems (SoS) approach considers the complex nature of the railway system, comprising interconnected subsystems like rolling stock and infrastructure. Neglecting these interdependencies risks sub-optimization of the overall system performance. Asset management of the railway system utilising a SoS approach ensures the focus of asset management on overall system requirements. The efficiency and effectiveness of the railway system is based on aspects such as availability, reliability, and safety performance. To enhance these aspects, monitoring, and improvement of key performance indicators (KPIs) emphasizing increased capacity and reduced operational costs is essential. The KPIs offer quantifiable parameters for performance optimization. Augmenting asset management through data-driven technologies can improve the efficiency and effectiveness of asset management. However, challenges persist in the implementation of data-driven solutions due to the railway system’s complexity and lack of a holistic perspective. A systematic performance-driven framework with a system-of-systems approach for augmented asset management of railway system provides handrail for the utilisation of data-driven technologies with railway system requirements at the centre while developing an asset management regime. The proposed framework aims to establish a clear relationship between system KPIs, and the performance of sub-systems and components aiding railway organizations in asset management design and implementation. This paper explains the important components of the proposed framework and demonstrates the application the framework for asset management and maintenance planning of high value components in the fleet of railway rolling stock. Adoption of the proposed framework is expected to enhance asset management through development and implementation of data-driven solutions that are aligned with system KPIs, to support asset management decision making.

Depth maps (DMs) are invaluable tools encapsulating scene information in a three-dimensional context. They have a crucial part in reconstructing the spatial layout of a scene, enabling a comprehensive understanding of object geometry. These DMs can originate from either a single image or a combination of multiple images, with the former approach referred to as monocular depth mapping. However, deriving accurate depth maps is a complex and ill-posed problem that often necessitates intricate calibration. Recent advances have turned to deep learning (DL) techniques to address these challenges. In the context of monocular depth estimation, we propose a novel methodology utilizing an Attention U-Net architecture (Attention UNet). By incorporating attention mechanisms, we bolster the network’s ability to extract salient features, particularly along object boundaries. Critically, this enhancement is achieved without introducing additional parameters to the networks, ensuring efficient model training. Our proposed approach is effective in producing high-quality depth maps with notable advantages. By leveraging the Attention UNet architecture, we substantially improve depth map accuracy, reducing the root mean square error (RMSE) by 0.23 on the benchmark NYU V2 dataset, Highlighting its supremacy compared to current state-of-the-art techniques.

Frequency deviation and Tie-Line power flow deviation are major concern due to the continuous load changing condition and the utilization of renewable energy sources in multi microgrid interconnected systems. Therefore, it is important and crucial to maintain the frequency and Tie-line power flow. In this paper, Novel hybrid algorithm combines both Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) driven proportional-integral-derivative (PID) controller and cascade Proportional Integral and Proportional Derivative (PI–PD) controller is suggested to deal with the issues in a proposed multi interconnected microgrid system. At first, the performance of the developed hybrid algorithm driven PID controller is investigated and its performance is compared with individual PSO and GWO driven PID controller. Finally the hybrid algorithm performance is investigated in cascade PI–PD controller and its performance is compared with the PID controller. Integral time multiplied by absolute error (ITAE) is used as the objective function in this work for obtaining optimum parameters of both PID and PI–PD controller. The simulated results show the superiority of the proposed hybrid algorithm (PSO–GWO) driven PI–PD controller compared with the other techniques in settling time, overshoot etc.

The occurrence of a Loss Of Offsite Power (LOOP) event can be a major threat to nuclear safety due to the dependence of auxiliary systems on electrical energy. Probabilistic safety assessments of nuclear power plants require, thus, estimates of the frequencies and durations of such LOOP events. These estimates are usually based on past statistical data, which is not always relevant. Model-based approaches are thus needed. This paper proposes an analytical method to estimate the frequency and duration of switchyard-centered LOOP events, which constitute one of the four main categories of LOOP events. The proposed method is mainly based on the identification of active minimal cut sets, considering the behavior of circuit breakers against faults according to their coordination and selectivity. Adapted versions of the Risk Reduction Worth and Fussel–Vesely importance factors are proposed to evaluate the impact of components on the switchyard-centered LOOP event frequency. Furthermore, uncertainty analysis is developed and performed. Various generic plant connection schemes are used for application. Results demonstrate the applicability of the methodology to estimate the frequency and duration of switchyard-centered LOOP events, and to identify optimal ways to reduce the risk by modifying the switchyard configuration.