Execution System Research Articles

A hybrid approach to investigating major management factors for effective highway preventive maintenance

To resolve the problem of the low management capacity of highway preventive maintenance (HPM), this paper identified and evaluated the major HPM management factors to improve management effectiveness and achieve sustainable highway development. The study conducted a literature review and exploratory factor analysis (EFA) to identify the major HPM management factors. Social network analysis (SNA) was used to distinguish the degree of importance of these factors. A system dynamics (SD) model was developed to explore their patterns of influence. The research identified six dimensions of HPM management, including the management system, management resources, management cognition, management decisions, management technology, and external conditions, along with 26 major management factors. Moreover, information acquisition, system perfection, etc., are key factors; system execution, manager capability, etc., are hub factors; and route selection, machinery allocation, etc., are non-key factors. These factors have a positive impact on HPM management, leading to an upward trend in management effectiveness. The main innovation provided a hybrid and comprehensive approach to identify and evaluate the major management factors for effective HPM. This study can guide managers in developing effective HPM plans, allocating resources more efficiently, improving the overall quality of highway maintenance and forming a sustainable transportation system.

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.

RT-APT: A real-time APT anomaly detection method for large-scale provenance graph

Advanced Persistent Threats (APTs) are prevalent in the field of cyber attacks, where attackers employ advanced techniques to control targets and exfiltrate data without being detected by the system. Existing APT detection methods heavily rely on expert rules or specific training scenarios, resulting in the lack of both generality and reliability. Therefore, this paper proposes a novel real-time APT attack anomaly detection system for large-scale provenance graphs, named RT-APT. Firstly, a provenance graph is constructed with kernel logs, and the WL subtree kernel algorithm is utilized to aggregate contextual information of nodes in the provenance graph. In this way we obtain vector representations. Secondly, the FlexSketch algorithm transforms the streaming provenance graph into a sequence of feature vectors. Finally, the K-means clustering algorithm is performed on benign feature vector sequences, where each cluster represents a different system state. Thus, we can identify abnormal behaviors during system execution. Therefore RT-APT enables to detect unknown attacks and extract long-term system behaviors. Experiments have been carried out to explore the optimal parameter settings under which RT-APT can perform best. In addition, we compare RT-APT and the state-of-the-art approaches on three datasets, Laboratory, StreamSpot and Unicorn. Results demonstrate that our proposed method outperforms the state-of-the-art approaches from the perspective of runtime performance, memory overhead and CPU usage.

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

Abstract Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

A federated information system framework for vertical integration

The convergence of operational and information technology in the era of Industry 4.0 has significantly increased the volume of data being generated on the manufacturing shop floors. Current research trends have focused on integrating this data with information present throughout the different systems in manufacturing facilities from Enterprise Resource Planning (ERP) to Manufacturing Execution System (MES) software, otherwise known as vertical integration. More specifically, previous works have looked at aggregating this data being read from Open Platform Communication Unified Architecture (OPC UA) servers by utilizing centralized aggregation servers to create information systems. This article, however, proposes a framework that adopts a federated information system designed to fulfill the manufacturing industry’s requirements and is capable of connecting to different OPC UA servers and storing the data within knowledge graph-based information models. GraphQL servers are also utilized in this system for easy accessibility of information by users. This system provides modularity, scalability, and interoperability to supplement the capabilities of information systems previously implemented. A prototype was also developed in this article for a federated information system for a boiler monitoring system.

BERTopic과 색채분석 기반 감성 추출을 이용한 관광지 추천 시스템 개발

This study aims to develop a personalized tourist destination recommendation system by analyzing the color and emotional factors of destination images. It evaluated and analyzed the emotional responses and color characteristics conveyed by images of natural resorts and urban tourist sites. A survey was conducted to collect users' emotional responses to these images, and a BERTopic model based on BERT was used to analyze text data from social media. The survey results provided a clear understanding of how users react to specific colors and emotional factors. For natural resorts, Green(G) was the most associated color, followed by Green-Yellow(GY) and Blue(B). In the case of urban tourist sites, Yellow-Red(YR) and Yellow(Y) were most commonly associated, followed by Blue-Green(BG) and Blue(B). Additionally, during the system's execution, the Bhattacharyya distance was used to measure color similarity between images, displaying images that match the user's emotions. Therefore, this study developed a recommendation system that considers the impact of destination images on users' emotions, proposing a new approach to better reflect users' emotional needs.

Opportunities and Challenges of CSR Digitalization: Lessons from Major Companies in Indonesia in Mitigating Post-COVID-19 Unemployment

In the aftermath of the COVID-19 pandemic, global impacts extended beyond communities' physical and mental health, encompassing nations' economic stability. Many companies responded actively by contributing to government efforts in addressing post-pandemic economic challenges, particularly through Corporate Social Responsibility (CSR) initiatives. This research aims to identify opportunities and challenges in digitalizing CSR to mitigate the financial impacts of the COVID-19 pandemic. The study focuses on the CSR program, Indosat Digital Camp (IDCamp), implemented by Indosat Ooredoo. The objective is to evaluate the opportunities and challenges faced by Indosat Ooredoo in digitizing CSR to address the economic impacts of the COVID-19 pandemic. Utilizing a qualitative methodology and a single case study design, the research involves interviews with 6 representatives from Indosat Ooredoo and 10 IDCamp participants from Yogyakarta, Bandung, Jakarta, Kabupaten Ogan Komering Ulu, and Kota Tangerang. Research findings reveal that digitalizing CSR presents sustainable opportunities as it can reduce unemployment, provide industry-relevant competencies, align with Indonesia's digital era, remain relevant to post-COVID-19 digital activities, align with government programs, support the demand for digital talents, and attract community participation due to its practical nature. However, IDCamp implementation poses potential challenges, including responsive organization, internet network issues, government program support, program updates, established execution systems, coordination with mass media, and ensuring digitalization facilitates community participation in CSR programs. Keywords: COVID-19 pandemic, CSR digitization, CSR Collaboration, CSR opportunities, CSR challenges.

Enhancing tourism management through big data: Design and implementation of an integrated information system

Many sectors, particularly traveling, have been profoundly affected by the fast development of big data technologies. This article provides a detailed examination of the development and execution of a Tourism Management Information System (TMIS) that utilizes big data analytics to improve operational effectiveness, gain insights into tourist behavior, and encourage sustainable tourism practices. The system combines data from several sources, including booking systems, social media platforms, and tracking devices, and addresses issues related to data privacy, quality, and integration. The TMIS utilizes statistical information to provide tailored tourist experiences, enhance resource allocation, and aid in long-term planning. The study emphasizes the significance of strong data management, which involves assuring the precision of data and its immediate significance. Additionally, this article examines the potential of big data in promoting a strong and creative tourism industry. It highlights the importance of future studies in exploring sustainable uses of big data and the incorporation of novel technologies, such as artificial intelligence, to improve personalized experiences. The present research highlights the significant and positive influence of big data on the management of tourism. It offers a structured approach for implementing big data in practice and sets the stage for long-term and sustainable growth in the tourism sector.

Research on Flexible Data Model of MES System based on Time-series Database

In the process of advancing towards digitization and intelligence in manufacturing, Manufacturing Execution Systems (MES) have gradually become the core of production processes. The design of the data model is crucial as it affects the scalability, maintainability, and stability of the system. This study focuses on addressing issues in MES systems such as high investment costs, imperfect data models, lack of flexibility, and inefficient data flow. It proposes a novel flexible data model based on time-series databases.This paper introduces the definition and characteristics of time-series databases, analyzes the shortcomings of existing MES system data models, and proposes a novel flexible MES data model based on time-series databases. Finally, the practicality and efficiency of the new data model are validated through experiments.

О СИСТЕМНОМ СТАТУСЕ ОБЪЕКТА ПРОГРЕССИВНОЙ СИСТЕМЫ ИСПОЛНЕНИЯ И ОТБЫВАНИЯ ЛИШЕНИЯ СВОБОДЫ

In the legal literature, the model of a progressive system of execution and serving of imprisonment is increasingly associated with a change in the legal status of a convicted person serving a criminal sentence, depending on the positive or negative behavior of the convicted person towards improvement or deterioration. Such judgments, in the author’s opinion, are not supported by the study of the progressive system from the position of the general definition of the system as a set of elements in mutual relations and connections forming a certain integrity, unity, as well as its understanding in the aspect of system analysis. To create a systematic understanding of the object of a progressive system with its systemic and legal characteristics, it is necessary to study it from the standpoint of system analysis, which is a means of improving and developing the current state of the progressive system as a whole (increasing its effectiveness). In the domestic legal literature, the systemic status of the object of the progressive system is not studied from the indicated positions. To show more clearly the features of the inseparability of system analysis from the object of the system (a person in custody; a person sentenced to imprisonment; For the first time, an attempt was made to create a systemic legal model of the systemic status of an object of a progressive system. In addition, the definition of the concept of the systemic status of an object of a progressive system is constructed, as well as its author’s concept is proposed, the structural elements of the systemic status of an object of a progressive system proposed for discussion are analyzed. The judgments given in the study have not yet found their completeness and are debatable, but their comprehensive study will allow us to take a different look at the reflection of the current concept of a progressive system in the norms of domestic legislation.

Lean Manufacturing Tools as a Driver of the Company’s Digital Transformation

The article substantiates the impact of applying lean manufacturing tools on the digital company’s transformation. The study distinguishes the main lean manufacturing tools that contribute to the optimisation of production processes, reduce losses and improve staff efficiency. Special attention is paid to the synergy of lean methods and digital technologies, which allows companies to adapt to changing market conditions and remain competitive by accelerating and replicating production processes in their digital transformation. The case study of implementing lean manufacturing tools at the Russian food industry enterprises, participants in the national project “Labour Productivity,” demonstrates the feasibility of implementing the key 5 lean manufacturing principles (sorting, organising, standardising, keeping clean and improving), along with the Manufacturing Execution System (MES). The author concludes that companies need to combine lean manufacturing and digital technologies to improve efficiency. Given the fact that lean manufacturing and information technology can conflict, providing their integration will increase the success of the digital company’s transformation. A lean manufacturing approach simplifies production processes, improves the efficiency of digitalisation and increases the likelihood of successful adoption of Industry 4.0 technologies.

ADVANCED SMART PARKING BARRIERS: DESIGN, IMPLEMENTATION, AND IMPACT ON URBAN INFRASTRUCTURE

This study investigates the creation and execution of an intelligent individual parking barrier system aimed at resolving urban parking difficulties in Malaysia. The project adheres to a systematic methodology that includes conceptual design, detailed design, prototype creation, testing and validation, data analysis and refinement, and implementation and deployment. The system's lifetime and efficacy are guaranteed by the inventive use of durable materials and advanced sensor technologies. Additionally, convenience and security are enhanced by a user-friendly wireless control system. The data analysis confirms that the system is highly reliable, with sensors that have a 95% success rate in detecting events and low delay in wireless control operation. The concept is in line with larger smart city projects, such as the Kuala Lumpur Smart City Blueprint, which aims to promote parking options that are both efficient and secure. In addition, the system promotes environmental sustainability by minimizing emissions related to the search for parking. It also aligns with the United Nations Sustainable Development Goals, namely SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). This study showcases the possibility of implementing the proposed system on a large scale in different urban areas in Malaysia, therefore leading to the development of more intelligent and effective urban infrastructure.

On-device training of artificial intelligence models on microcontrollers

<p><span>Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.</span></p>

Implementing Manufacturing Execution Systems (MES) for Industry 4.0: Overcoming buyer-provider information asymmetries through knowledge sharing dynamics

Manufacturing Execution Systems (MES) have been considered the ‘entrance door’ to the Industry 4.0 journey. As part of this journey, this system must operate in real-time and be integrated with several other systems, resulting in modular adaptation and customized implementation of this solution. This increased complexity underscores the importance of the relationship between companies and their technology providers, requiring intensive knowledge-sharing (KS) activities between the parties. In particular, information asymmetry between buyers and MES 4.0 providers may be critical for the successful implementation of the system, but little is known about this issue, which has a high impact on the first stage of the Industry 4.0 journey. Thus, we aim to understand how knowledge sharing affects information asymmetry between buyers and technology providers for MES 4.0 implementation. Our investigation is based on qualitative interviews with 56 key experts from 33 companies, which allowed us first to define MES configurations for Industry 4.0, i.e., MES 4.0 as a differentiation from early versions of MES. Then, we conducted a multiple case study with three buyer-provider dyads in the pre-and post-contract phases of MES purchasing to analyze KS dynamics during the implementation of MES 4.0. We propose a model that explains the relationship between MES 4.0 complexity and KS intensity and a decision framework that describes the steps for MES 4.0 implementation while focusing on reducing information asymmetry during the collaboration.

A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems

Conventional Digital Twins (DTs) in smart manufacturing rely on complex and time-intensive simulation models, hindering real-time DT-based decision-making. However, the availability of big data in Manufacturing Execution Systems (MES) enables training different Machine Learning (ML) models for fast and accurate predictions and decision assessments. Accordingly, this paper proposes an ML-Based Simulation Metamodeling Method (MLBSM) to facilitate DT-based decision-making for dynamic production scheduling in complex Stochastic Flexible Job Shop (SFJS) environments. The proposed MLBSM integrates three modules: a novel data vectorizing method (SPBM), multi-output Adaptive Boosting Regressor (ABR) models, and a new statistical risk evaluation method. SPBM converts unstructured production log data into numerical vectors for ABR training by calculating numeric penalty scores for each job based on the position of operations in the schedule queue. Each trained ABR predicts mean job completion times for various dynamic scenarios based on shift schedules. The risk evaluation method estimates the standard deviation of job completion times and calculates the delay probability scores for each job, aiding DT in promptly evaluating production schedules. Working seamlessly together, MLBSM modules present a novel way of using production log data for ML training and ultimately bypassing several computationally intensive simulation replications. In this research, a simulation model generates the synthetic MES data, focusing on the machining process at a photolithography workstation in the semiconductor manufacturing. Experiments demonstrate the MLBSM’s accuracy and efficiency, predicting high-risk jobs with over 80% recall and being at least 70 times faster than conventional simulation runs. Sensitivity analyses also confirm the MLBSM’s consistency under different workstation conditions.

Sociology of E-Health Initiatives in Sierra Leone’s Communal Health Zone

The Sierra Leonean government has promoted diverse programs and approaches as a segment of civic automation ambitions to hand associates of the communal with a reachable path to health social data and services. Accomplishments have been made to grasp Datalogies in Sierra Leone’s communal health zone. The conduct of social health establishments in Sierra Leone nonetheless persists in degenerating at a moment when the exploit of Datalogies in social health is attaining motivation in the Mano River Basin. This sociological inquiry, therefore, urges to evaluate the execution of the social E-health system disclosing the ongoing state of social e-health or electronic device networks in Sierra Leone’s communal clinics. The sociological inquiry embraced an exploratory social inquiry design. Respondents in this inquiry preferred purposive sampling. Social data were amassed via respondents' social observations, in-depth dialogues, and documentary inquiry. The inquiry determined that despite social e-health being an essential instrument in enhancing standard, swift, and productive social health care, its achievement in Sierra Leone has not been smooth, and it is still in its immaturity platforms. The inquiry wrapped up with the fact that social e-health is a dependable instrument for forwarding prosperous and efficient communal social health service supply in Sierra Leone. The sociological inquiry, therefore, mirrors and offers aspects imperative for the thriving application of social E-health in Sierra Leone’s communal social health zone.

Experimental backward integration for state-dependent differential Riccati equation (SDDRE): A case study on flapping-wing flying robot

Backward integration (BI) is a classical approach for solving optimal control arising from linear quadratic regulator (LQR) design in differential form. It proposes a two-round solution, the first one starting from a final boundary condition to the initial one to generate the optimal gain, and the next one, solving the control system in a forward loop. Implementation of the BI on the nonlinear optimal control, the state-dependent differential Riccati equation (SDDRE), is a challenge since in the backward loop, the state information is missing and is not straightforward like the LQR. Hence, a control for backward motion is required to regulate the system from the terminal to the initial desired states. While there have been some valuable works on the theoretical implementation of the BI in simulations for the SDDRE, this approach has not been reported in experiments for the best knowledge of the authors. Here in this work, the SDDRE is solved using the BI and two backward and forward solution steps, and it is experimentally applied for a flapping-wing flying robot (FWFR). The execution of the control system is done onboard using Raspberry Pi 4B as the processor which has limited computational capacity. The trajectory tracking of a line in a closed limited space is proposed for the FWFR flight. The objective is to position the robot bird at the corner of the rectangular limited space with minimum error in translation. The results have been presented for 21 flights to show the repeatability and presented the best-case minimum error of 10 (cm) at the end of the trajectory, in the YZ-plane. Considering approximately 12 (m) flight path, the error is found less than 1 percent of the travel distance. The results were compared with forward integration to confirm the correctness of the computation. The experimental flight dataset, MATLAB simulation codes, and experimental Python codes are available as supplementary material for this work in the online version of the paper.

Development and execution of a notification system for lectures in universities

The aim of this work is design and implementation of a lecture alert system for Computer Science Department which makes use of Short Message Service (SMS) to enable lecturers to know when they have lectures and also deliver the venue, department, day, date and time to their phones. The motivation for this work is due to the challenges of the consistence report of errors in the process of filling and allocating the lectures on the time table, lack of effective record system or database system to store the record of time table and clashes of lecture because one venue can be allocated to different lecturers at the same time. The methodology that will be employed in this study is the object-oriented analysis and design methodology (OOADM) which will make use of the following programming languages; JavaScript for controls and flexibility, PHP for effective linking and communication with the database machine, HTML for browser communicator, a database machine, MySQL, and macromedia flash (11.0) for the video output display. The expected result that will be obtained is to a design a conventional lecture alert system using Short Messaging Service (SMS), also to provide a new method of checking lectures using mobile phone short messaging service work and to alert students ahead of their lectures and inform them of the venue of the lecture.

Controllable region analysis of active front steering and differential braking system stability control characterized by additional yaw moment

Active Front Steering (AFS) and Differential Braking System (DBS) are commonly used execution systems for vehicle anti-rollover or handling stability control. They regulate the vehicle’s yaw moment by adjusting the lateral tire force and longitudinal tire force, respectively. However, these two systems differ in terms of control capabilities and regulatory sensitivities. In emergency conditions, it becomes crucial to allocate and coordinate the tasks for the execution systems according to their yaw moment control capability and adjustment sensitivity. This paper proposes a method for calculating the controllable region represented by the additional yaw moment. This method allows for the computation of the control capability of AFS and DBS based on the current state. Additionally, the influence of different vehicle states on the controllable regions of the two execution systems is analyzed. Building upon this analysis, the yaw moment adjustment sensitivities of AFS and DBS systems under the current state are further investigated. Finally, a task coordinated strategy between AFS and DBS is developed based on the analysis of controllable regions and sensitivities of different execution systems. Simulink simulations are conducted on yaw stability and roll stability conditions to comprehensively analyze the working process of the proposed task coordination strategy.