Hasil untuk "Industrial engineering. Management engineering"

A. Padovano, Martina Cardamone

Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Y. Stamatiou

The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies.

Jun Wang, Peng Wu, Xiangyu Wang et al.

Wang Qingcheng, Lou Yi, Zhang Deqing et al.

Electric field manipulates the change of droplets wettability on superhydrophobic surfaces, which is widely used in many fields such as electronic zoom microlens and electro wetting displays, and has an important research value. This paper prepared a superhydrophobic acetate film applied electrostatic spinning technique,the maximum contact angle of acetate film is 152.6°, conducted electric field-regulated water droplet wettability change tests, applied voltage to water droplets on acetate film. it was found that the contact angle of the droplets decreased with the increase of the electric field strength, and the electric field regulated the contact angle of the droplets to change in the range of 92.7-142.3°. When the power supply is turned off, the contact angle of the droplet can gradually recover, but not completely restored to the original state.

Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ahmed Ali Ahmed et al.

Abstract Vehicular Ad-Hoc Networks (VANETs) have facilitated the massive exchange of real-time traffic and weather conditions, which have helped prevent collisions, reduce accidents, and road congestions. This can effectively enhance driving safety and efficiency in technology-driven transportation systems. However, the transmission of massive and sensitive information across public wireless communication channels exposes the transmitted data to a myriad of privacy as well as security threats. Although past researches has developed many vehicular ad-hoc networks security preservation schemes, several of them are inefficient or susceptible to attacks. This work, introduces an approach that leverages reverse fuzzy extraction, bilinear pairing, and Physically Unclonable Function (PUF) to design an efficient and anonymity-preserving authentication scheme. We conduct an elaborate formal security analysis to demonstrate that the derived session key is secure. The semantic security analyses also demonstrate its resilience against typical VANET attacks such as impersonations, denial of service, and de-synchronization, instilling confidence in its effectiveness. Moreover, our approach incurs the lowest computational overheads at relatively low communication costs. Specifically, our protocol attains a 66.696% reduction in computation costs, and a 70% increment in the supported security functionalities.

Ali AbuGneam, Somayeh Nemati, Afshin Babaei

In this research, we propose a new numerical method for solving a class of distributed-order fractional partial differential equations, specifically focusing on distributed-order time fractional wave-diffusion equations. The method begins by introducing a novel generalization of Bernoulli wavelets and deriving an exact result for the Riemann–Liouville integral of these new basis functions. Utilizing the Gauss–Legendre quadrature formula and a strategically chosen set of collocation points, along with approximations for the unknown function and its derivatives, we transform the problem into a system of algebraic equations. An error analysis is then conducted for the approximation of a bivariate function using fractional-order Bernoulli wavelets. Finally, three examples are solved to demonstrate the method’s applicability and accuracy, with the numerical results confirming its effectiveness. These findings demonstrate that the parameters of the basis functions can be adjusted to suit the given problem, thereby enhancing the accuracy of the method.

Ifiok Udoidiok, Fuhao Li, Jielun Zhang

Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning and proposed various defense or evaluation methods; however, most efforts remain limited to quantifying performance degradation, with little systematic comparison of internal behaviors across model architectures under attack and insufficient attention to interpretability for revealing model vulnerabilities. To tackle this issue, we build a reproducible evaluation pipeline and emphasize the importance of integrating robustness with interpretability in the design of secure and trustworthy ML systems. To be specific, we propose a unified poisoning evaluation framework that systematically compares traditional ML models, deep neural networks, and large language models under three representative attack strategies including label flipping, random corruption, and adversarial insertion, at escalating severity levels of 30%, 50%, and 75%, and integrate LIME-based explanations to trace the evolution of model reasoning. Experimental results demonstrate that traditional models collapse rapidly under label noise, whereas Bayesian LSTM hybrids and large language models maintain stronger resilience. Further interpretability analysis uncovers attribution failure patterns, such as over-reliance on neutral tokens or misinterpretation of adversarial cues, providing insights beyond accuracy metrics.

Corina Pacher, M. Woschank, B. Zunk et al.

ABSTRACT The holistic integration of the human workforce in digital and sustainable manufacturing and logistics systems requires structured approaches for the systematic (re-)alignment of professional education initiatives. Engineering education is seen as a critical investment for the further development of industry and society. However, future engineers will require more than just a strong technological background. Empirical-based success factors that contribute to the holistic reorientation of engineering education are typically researched in a fragmented manner and the focus on competence-orientation is frequently overlooked, especially in the Industrial Engineering and Management (IEM) discipline. In this paper, the authors conduct a systematic literature review on engineering education with an emphasis on competence-based engineering education in the IEM discipline. Moreover, the authors provide fruitful implications for the competence-based realignment of engineering education, present an amalgamated overview of competences that are considered necessary for future engineering education, and provide a set of novel methods and tools.

J. de Curtò, I. de Zarzà, Juan-Carlos Cano et al.

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.

Yao He, Jing Yang, Shaobo Li et al.

Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the parameters important to the previous task to avoid catastrophic forgetting has a positive effect; existing regularization methods cause the gradient to be near zero because the loss is at the local minimum. To solve this problem, we propose a new continuous learning method with Bayesian parameter updating and weight memory (CL-BPUWM). First, a parameter updating method based on the Bayes criterion is proposed to allow the neural network to gradually obtain new knowledge. The diagonal of the Fisher information matrix is then introduced to significantly minimize computation and increase parameter updating efficiency. Second, we suggest calculating the importance weight by observing how changes in each network parameter affect the model prediction output. In the process of model parameter updating, the Fisher information matrix and the sensitivity of the network are used as the quadratic penalty terms of the loss function. Finally, we apply dropout regularization to reduce model overfitting during training and to improve model generalizability. CL-BPUWM performs very well in continuous learning for classification tasks on CIFAR-100 dataset, CIFAR-10 dataset, and MNIST dataset. On CIFAR-100 dataset, it is 0.8%, 1.03% and 0.75% higher than the best performing regularization method (EWC) in three task partitions. On CIFAR-10 dataset, it is 2.25% higher than the regularization method (EWC) and 0.7% higher than the scaled method (GR). It is 0.66% higher than the regularization method (EWC) on the MNIST dataset. When the CL-BPUWM method was combined with the brain-inspired replay model under the CIFAR-100 and CIFAR-10 datasets, the classification accuracy was 2.35% and 5.38% higher than that of the baseline method, BI-R + SI.

Enrique González-Plaza, David García, Jesús-Ignacio Prieto

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using eight-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.

José Antonio Rojas García, José Luis Ajuria Foronda, Jon Arambarri

El objetivo de la investigación fue diseñar e implementar una metodología basada en la transformación digital de forma ágil y en un corto periodo que permita a las pymes del sector de logística ligera del Perú incrementar su competitividad bajo un enfoque de investigación mixto con un diseño exploratorio secuencial (DEXPLOS), observacional y experimental. La población de estudio estuvo constituida por 750 pymes, la muestra estuvo conformada por 255 empresas y se realizó un muestreo probabilístico estratificado. Los criterios de inclusión fueron contar con estrategias competitivas definidas, un año de operación como mínimo y licencias de funcionamiento y código postal. El instrumento de investigación fue un cuestionario compuesto por 189 preguntas distribuidas en variables, tales como estrategia, rentabilidad, nivel técnico, productividad, calidad y trazabilidad. Se concluye que la implementación de la metodología propuesta permitió la transformación digital de las empresas objeto de estudio en un plazo de cuatro meses, por lo tanto, incrementaron su competitividad.

J. Deuse, Nikolai West, Marius Syberg

Industrial Engineering, through its role as design, planning and organizational body of the industrial production, has been crucial for the success of manufacturing companies for decades. The potential, expected over the course of Industry 4.0 and through the application of Data Analytic tools and methods, requires a coupling to established methods. This creates the necessity to extend the traditional job description of Industrial Engineering by new tools from the field of Data Analytics, namely Industrial Data Science. Originating from the historic pioneers of Industrial Engineering, it is evident that the basic principles will remain valuable. However, further development in view of the data analytic possibilities is already taking place. This paper reviews the origins of Industrial Engineering with reference to four pioneers, draws a connection to current day usage, and considers possibilities for future applications of Industrial Data Science.

Imam Agustian Nugraha, Vidilla Rosalina, Suherman

Pelayanan jasa informasi meteorologi yang cepat, tepat akurat dan mudah dipahami merupakan salah satu tugas Stasiun Meteorologi (STAMET) Kelas I Maritim Serang. Namun berdasarkan wawancara dan observasi yang dilakukan, menunjukan bahwa pelayanan yang dilakukan belum efisien dan praktis karena pengguna jasa harus datang ke kantor untuk memperoleh data yang diinginkan. Selain itu petugas pelayanan masih mencatat transaksi dalam buku yang kemudian diketik kembali pada aplikasi Microsoft Word, dan mengalami kesulitan dalam mencari berkas yang akan diberikan kepada pengguna jasa. Penelitian ini bertujuan untuk membuat sistem yang dapat membantu tugas STAMET untuk meningkatkan pelayanan jasa meteorologi dengan menggunakan pendekatan Customer Relationship Management (CRM). Metode pengembangan yang digunakan yaitu model prototype dengan perancangan diagram Unified Modelling Language (UML). Sistem dibuat menggunakan Framework Codeigniter dengan Bahasa pemograman Hypertext Preprocessing (PHP) sebagai Server side Programing dan MySQL sebagai Database Server. Metode pengujian sistem yang digunakan adalah Blackbox. Hasil akhir yang diharapkan adalah sebuah sistem informasi pelayanan jasa meteorologi berbasis Web, yang dapat membantu pengguna jasa mendapatkan informasi meteorologi, mempermudah petugas dalam memberikan pelayanan jasa, pembuatan jadwal kunjungan atau pertemuan, mengajukan komplain pelayanan jasa, memberikan kritik dan saran serta mengukur kepuasan pengguna jasa terhadap pelayanan yang diberikan oleh petugas STAMET Kelas I Maritim Serang.   Kata Kunci: Customer Relationship Management (CRM), Pelayanan Jasa Meteorologi, Sistem

G. Elia, A. Margherita, G. Passiante

Engineering management has been historically focused on integrating scientific, engineering, and management know-how to contribute effectively to the functioning of organizations and industries. Today, an array of disruptive socio-technological transformations is bringing new challenges for education and research institutions engaged to advance the knowledge needed to design more performing socio-technical systems. Framed within the larger industrial engineering domain, management engineering has emerged as a new perspective to integrate technological and managerial knowledge, although a shared understanding of the field is yet to be introduced. In particular, the meaning and building blocks of management engineering can be discussed in the light of the evolving nature of engineering management. This article presents an extensive work of search and analysis of academic and practitioner evidence about management engineering, which allowed to derive a taxonomy of 467 concepts and 32 aggregating topics. The proposed framework can support academic discussion on the relevance of integrating management and engineering knowledge in the current socio-technical scenario. For practitioners, the identification of management engineering topics can be used to design global education initiatives as well as competence development and professional certification processes.

Laura Saukko, K. Aaltonen, H. Haapasalo

PurposeThe purpose of this paper is to achieve an understanding of the challenges and preconditions for inter-organizational collaborative project practices in industrial engineering projects. A framework for identifying the challenges and preconditions for inter-organizational collaboration is presented.Design/methodology/approachThe adopted research method is qualitative, and empirical data were collected from the industrial engineering project sector in Finland. The literature related to industrial engineering projects and inter-organizational collaborative project management practices is summarized, informing the qualitative design of the study.FindingsBy analyzing empirical data from industrial engineering projects, the challenges for inter-organizational collaboration are identified in each industrial engineering project stage. A framework of preconditions for inter-organizational collaboration is identified, in which investors are advised to pay attention when deciding on the use of collaborative project management methods.Practical implicationsThe findings of this study help practitioners deal effectively with mechanisms aimed at fostering and hindering inter-organizational collaborative practices. The identified preconditions for inter-organizational collaboration provide support for decision-making in every phase of an engineering project and can be used as guidelines throughout the process.Originality/valueInter-organizational collaborative project management practices have recently been attracting attention in the industrial engineering project setting. This research is an attempt to identify the underlying forces supporting and preventing inter-organizational collaboration in industrial engineering projects. This study offers a framework that can help academics and project management practitioners deal with the challenges affecting inter-organizational collaboration at each project stage and consider preconditions for inter-organizational collaboration in industrial engineering project settings.

Kamel Arafet, Rafael Berlanga

The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Industry 4.0 allows a substantial development in automatic diagnostic systems. The objective of this work is to obtain the DT of a Photovoltaic Solar Farm (PVSF) with a deep-learning (DL) approach. To build such a DT, sensor-based time series are properly analyzed and processed. The resulting data are used to train a DL model (e.g., autoencoders) in order to detect anomalies of the physical system in its DT. Results show a reconstruction error around 0.1, a recall score of 0.92 and an Area Under Curve (AUC) of 0.97. Therefore, this paper demonstrates that the DT can reproduce the behavior as well as detect efficiently anomalies of the physical system.

Yoomi Park, Heewon Seo, Kyunghun Yoo et al.

Abstract Some of the variants detected by high-throughput sequencing (HTS) are often not reproducible. To minimize the technical-induced artifacts, secondary experimental validation is required but this step is unnecessarily slow and expensive. Thus, developing a rapid and easy to use visualization tool is necessary to systematically review the statuses of sequence read alignments. Here, we developed a high-performance alignment capturing tool, CaReAl, for visualizing the read-alignment status of nucleotide sequences and associated genome features. CaReAl is optimized for the systematic exploration of regions of interest by visualizing full-depth read-alignment statuses in a set of PNG files. CaReAl was 7.5 times faster than IGV ‘snapshot’, the only stand-alone tool which provides an automated snapshot of sequence reads. This rapid user-programmable capturing tool is useful for obtaining read-level data for evaluating variant calls and detecting technical biases. The multithreading and sequential wide-genome-range-capturing functionalities of CaReAl aid the efficient manual review and evaluation of genome sequence alignments and variant calls. CaReAl is a rapid and convenient tool for capturing aligned reads in BAM. CaReAl facilitates the acquisition of highly curated data for obtaining reliable analytic results.

Asep Saeful Milak, Eka Wahyu Hidayat, Aldy Putra Aldya

Game adalah salah satu media teknologi yang populer di kalangan masyarakat baik dari anak kecil maupun orang dewasa sebagai media hiburan. game itu sendiri memiliki banyak sekali genre atau jenisnya. Penelitian ini mengembangkan game dengan menerapkan Artificial Intelligence pada game racing. Game sejenis yang telah diobservasi memiliki kelemahan bersifat endless run dan juga tidak memiliki NPC (Non-Player Character) didalamnya. Selain itu, alasan penelitian ini dibuat karena untuk mengangkat permainan tradisional yang hampir dilupakan, untuk itu perlu dibuat game sejenis yang berbeda dari sebelumnya dengan menambahkan NPC didalam game dengan menerapkan beberapa Algoritma. Di dalam penelitian ini menerapkan Algoritma Collision Avoidance System untuk membuat NPC dapat menghindari Obstacle dan Algoritma Random Number Generator untuk membuat Obstacle muncul secara acak. Dalam penelitian ini berhasil membuat “Balap Egrang” dengan Metode Game Development Life Cycle (GDLC). Berdasarkan pengujian yang telah dilakukan, hasil pengujian alpha sudah sesuai secara fungsional dan dari pengujian beta hasil uji fungsionalitas User Acceptence Test (UAT) didapat nilai sebesar 82,58% dinyatakan layak untuk digunakan dengan interpretasi “Baik” yang berarti game ini layak digunakan dan dapat dikembangkan.   Abstract Egrang game is one of the traditional games in Indonesia that uses two meters of bamboo material and is given a footing below. This game can be played by all ages by stepping on the footing and maintaining balance when walking at a certain distance and time. This game has begun to erode the era and needs to be preserved. This study tries to revive traditional games into digital-based games by implementing Artificial Intelligence. Similar games that have been observed, such as the Balap Karungs Game from Playstore which is in the racing genre, have endless run weaknesses and also have no Non-Player Character NPCs. The results of this study are Android-based games by applying the Collision Avoidance System Algorithm to make NPCs avoid the Obstacle and Random Number Generator Algorithms to make the Obstacle appear randomly. The development of the Balap Egrang Game uses the Game Development Life Cycle (GDLC) method. Based on the tests that have been carried out, the alpha test results are functionally appropriate and from the beta testing the results of the User Acceptence Test (UAT) functionalities obtained a value of 82.58% which is declared feasible to use with the interpretation of "Good" which means that the game is feasible developed..

A. Alves, F. Moreira, M. Carvalho et al.

This work has been supported by FCT – Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019