Hasil untuk "Industrial engineering. Management engineering"

Lei Zhang, Xiaosong Hu, Zhenpo Wang et al.

Jian-long Guo, Y. Bao, Min Wang

Steel slag is the main waste product in the steelmaking process. Because of its chemical composition and technical properties, it can be reused as raw material in steel plants and can serve as a substitute for aggregates in civil engineering. In this paper, we reviewed steel slag treatment, recycling, and management in China. Although China's annual slag production reached more than 100 million tons, its utilization rate is only 29.5%. As of 2016, more than 300 million tons of steel slag have not been used effectively. Large steel slag emissions are causing environmental problems for China. China's steel slag utilization rate is low compared with that of industrial countries: the utilization rate is 98.4% in Japan, 87.0% in Europe, and 84.4% in the United States. Compared with other nations, China also has a gap in its usage of slag in road construction and agriculture. Although the Chinese government has been active in creating a legislative and institutional framework to realize effective steel slag treatment and recycling, these efforts are limited. Outdated treatment approaches is one of the reason for low utilization rate in China, most Chinese steel plants carry out the preliminary treatment (like family workshops) of steel slag, no one system can be used for all ferrous waste recovery, and 47% enterprises' steel slag stability after treatment do not meet requirements of follow-up product. Road construction issues caused by high costs and policy limited, legal restrictions and lack of standard on agricultural applications are other two reasons for low utilization rate of steel slag. New policies are needed to improve utilization rates. We propose the concept of gradual utilization to promote the effective utilization of steel slag.

Sunil Kumar, Stephen R. Smith, G. Fowler et al.

India faces major environmental challenges associated with waste generation and inadequate waste collection, transport, treatment and disposal. Current systems in India cannot cope with the volumes of waste generated by an increasing urban population, and this impacts on the environment and public health. The challenges and barriers are significant, but so are the opportunities. This paper reports on an international seminar on ‘Sustainable solid waste management for cities: opportunities in South Asian Association for Regional Cooperation (SAARC) countries’ organized by the Council of Scientific and Industrial Research-National Environmental Engineering Research Institute and the Royal Society. A priority is to move from reliance on waste dumps that offer no environmental protection, to waste management systems that retain useful resources within the economy. Waste segregation at source and use of specialized waste processing facilities to separate recyclable materials has a key role. Disposal of residual waste after extraction of material resources needs engineered landfill sites and/or investment in waste-to-energy facilities. The potential for energy generation from landfill via methane extraction or thermal treatment is a major opportunity, but a key barrier is the shortage of qualified engineers and environmental professionals with the experience to deliver improved waste management systems in India.

D. Ivanov, Christopher S. Tang, A. Dolgui et al.

ABSTRACT While Industry 4.0 has been trending in practice and research, operations management studies in this area remain nascent. Our intent is to understand the current state of research in Industry 4.0 in different disciplines and deduce insights and opportunities for future research in operations management. In this paper, we provide a focused analysis to examine the state-of-the-art research in Industry 4.0. To learn about researchers’ perspectives about Industry 4.0, we conducted a large-scale, cross-disciplinary and global survey on Industry 4.0 topics among researchers in industrial engineering, operations management, operations research, control and data science at the 9th IFAC MIM 2019 Conference in Berlin in August 2019. By using our survey findings and literature analysis, we build structural and conceptual frameworks to understand the current state of knowledge and to propose future research opportunities for operations management scholars. Glossary of Abbreviations AGV: Automated guided vehicle; AI: Artificial intelligence; APS: Advanced planning system: a wide variety of software tools and techniques, with many applications in manufacturing and logistics (including the service sector); BDA: Big data analytics; CAS: Complex adaptive system: a system composed of many interacting parts that evolve and adapt over time; CIM: Computer integrated manufacturing; CPFR: Collaborative planning, forecasting and replenishment; CPS: Cyber-physical system: a seamless integration of computation and physical components; DAMCLS: Decision analysis, modelling, control and learning systems; ERP: Enterprise resource planning; FMS: Flexible manufacturing system; I4.0: Industry 4.0; IFAC: International Federation of Automatic Control: a federation is concerned with the impact of control technology on society; IME: Industrial and mechanical engineering; IoT: Internet-of-Things; IT: Information technology; M2M: Machine-to-machine; MAS: Multi-agent system: a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver; OR: Operations research; RFID: Radio frequency identification: a technology that uses electromagnetic fields to automatically identify and track tags attached to objects; RMS: Reconfigurable manufacturing system: a manufacturing system that can change and evolve rapidly in order to adjust its productivity capacity and functionality; OM: Operations management; T&T: Track and trace system; VCA: VOS viewer co-occurrence analysis: a software tool for visualising bibliometric networks; VMI: Vendor-managed inventory.

F. V. Perevoshchikov, V. G. Bukreev

Currently, the development of new control approaches for asynchronous electric drives with stringent requirements for vibration-acoustic performance and spectral composition of autonomous inverter output currents represents a highly relevant research challenge. The key challenges in designing this class of electric drives stem from the relatively low effectiveness of existing technical solutions. This limitation arises either from constraints in current controller synthesis methods or from rigorous demands regarding power-to-weight and dimensional parameters. This paper presents an original method for generating control signals in an alternating current electric drive autonomous inverter. The proposed approach utilizes regulation based on the deviation of the generalized output voltage vector amplitude in the autonomous inverter. The synthesis procedure for such a controller begins with defining the desired closed-loop system transfer function. The system dynamic processes are determined by a characteristic polynomial that can be of arbitrary type. For comparative analysis, two controller types are examined: one based on a Butterworth filter and another utilizing a Newton polynomial. The study proposes employing bilinear transformation to implement the derived continuous functions in discrete form, enabling software implementation in Simulink and subsequent microprocessor-based execution. The developed model, which accounts for discrete control signal generation, has yielded the spectral composition of the drive converter output currents and voltage-frequency characteristics under parametric disturbances introduced by the control object. Results demonstrate that the Butterworth filter-based controller shows superior efficiency compared to both open-loop systems and closed-loop systems with Newton polynomial-based controllers. The obtained results can be effectively applied in the development of low-noise electric drives for specialized applications.

Xin Shi, Zhengchao Xu, Sunan Ge et al.

Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data is challenging to obtain. However, existing methods face two key limitations: data consistency preservation remains challenging for out-of-domain data, and degradation process alignment is difficult when the degradation mechanism is not mathematically predetermined. To address these issues, this paper presents a novel zero-shot image restoration method (FIR-SDE). Traditional generation-oriented diffusion models (designed for image creation) are replaced with restoration-oriented models (specialized for degradation repair), expanding the range of effectively restorable images. To mitigate the noise offset (discrepancies between real and model-simulated degradation) and to enhance the alignment, a multi-step optimization strategy is employed, which evaluates the distance between real and simulated degraded images via frequency domain distribution. Experiments were conducted on two image restoration tasks (image deraining and inpainting) using three public datasets (AFHQ-dog, CelebA, and FFHQ), with Gaussian blur and motion blur superimposed as noise offsets. Results demonstrate that FIR-SDE method outperforms competitive methods in restoration quality and noise resistance. By eliminating data space constraints and exhibiting robustness against noise offsets, FIR-SDE offers a more flexible and efficient solution to broaden the practical applicability of zero-shot image restoration.

Lars-Peter Meyer, Claus Stadler, Johannes Frey et al.

Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs surpass any other form of representation in terms of effectiveness. However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices. It also demands a significant amount of work. Considering the advancements in large language models (LLMs) and their interfaces and applications in recent years, we have conducted comprehensive experiments with ChatGPT to explore its potential in supporting KGE. In this paper, we present a selection of these experiments and their results to demonstrate how ChatGPT can assist us in the development and management of KGs.

Dwi Ratna Puspita Sari, Sirli Fahriah, Kurnianingsih et al.

Gold is one of the most favored investment instruments due to its stability and its ability to preserve value against inflation. However, its price movements are volatile and influenced by various global economic factors, currency exchange rates, and geopolitical conditions, making gold price forecasting a significant challenge. This study aims to develop a gold price forecasting system using the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that excels in processing time-series data. The dataset consists of historical daily gold buying and selling prices from 2015 to 2025, collected from Yahoo Finance, Logam Mulia, and the official website of Bank Indonesia. The modeling process follows the CRISP-DM methodology, which includes business understanding, data preparation and exploration, modeling, and evaluation stages. Time Series Cross Validation (TSCV) is used to validate the model. LSTM performance is compared with other models such as GRU, CNN-1D, and Simple RNN to identify the best-performing architecture. Evaluation results indicate that LSTM achieved the highest performance with an R² score of 0.99 for selling prices and 0.98 for buying prices on the final test dataset. The system is deployed online, making it accessible in real-time. This research is expected to assist investors, financial analysts, and the general public in making smarter investment decisions based on valid historical data and advanced forecasting technology.

George Tsamis, Georgios Evangelos, Aris Papakostas et al.

One significant digital initiative that is changing Greece’s tax environment is the myDATA platform. The platform, which is a component of the wider digital governance agenda, provides significant added value to enterprises and the tax administration, despite the challenges of adaption. Despite the positive response, we find that the development of the platform could have been carried out quickly and at a significantly lower cost and could have been able to cope much faster with the rapid and necessary changes that the platform will have to comply with. For these reasons, development in WordPress would be considered essential as this CMS platform guarantees a fast and developer-friendly environment. In this publication, as a contribution, we provide all the necessary information to develop a myDATA-like platform in a fast, economical and functional way using the WordPress CMS. Our contribution also contains the analysis of the minimum necessary amount of services of the myDATA platform in order to perform its basic functionalities, the description of the according database relational model, which must be implemented in order to provide the same functionality with the myDATA platform, and the analysis of available methods to quickly create the necessary forms and services. In addition, we study how to develop Artificial Intelligence mechanisms with a success rate reaching up to 90% for automatic tax violation detection algorithms.

Oyewole Oyekoya, Kwame Agyemang Baffour

Using look-alike avatars may enhance the likeability and realism of avatars in 3D virtual learning environments. This paper explores perception of the features of the look-alike avatar representations of an instructor in virtual environments in two studies. In a pilot study, an instructor was represented as a look-alike, stick, and video avatar, allowing us to investigate students’ perceptions of teaching effectiveness in virtual and augmented reality environments. The main study seeks to determine the influence of three specific features of a look-alike avatar (head shape, texture fidelity and head orientation) on perception of likeability and visual realism, especially when judged by other people. Two textured look-alike avatars were generated using: (i) three-dimensional (3D) stereophotogrammetry; and (ii) 3D face reconstruction from a single full-face image. Participants compared three different head orientations (0°, 45°, 90°) of the look-alike avatars’ textured heads to their corresponding head silhouettes, to emphasize the differences in head shapes. Results suggest that participants prefer geometrically-accurate photorealistic avatars of the instructor due to the accuracy of the head shape and texture fidelity. In line with studies on face recognition, participants ranked the likeability and realism of the look-alike avatars similarly regardless of the head orientation. We discuss the implications of these findings for 3D virtual learning environments.

Hajar Ebrahimi, Fahimeh Babalhavaeji, Dariush Matlabi et al.

Objective: This study aims to investigate the factors influencing professional publishing and propose a model for professional book publishing in Iran. Method: The research was conducted using a survey method with a researcher-developed questionnaire. The statistical population consisted of professional publishing managers in the country, totaling 581 publishers, from which 231 were selected as a sample based on Cochran's formula. Ultimately, 211 questionnaires were completed and analyzed. Data processing was carried out using SPSS software, employing exploratory the factor analysis and multiple regression test. Findings: Based on exploratory factor analysis, nine factors have been identified as influential in professional publishing: the economics of publishing, the supply and display of publishing products, government support and backing, adherence to copyright, publishing evaluation and auditing, advertising, marketing and branding, publishing management, and the creation of publishing content. Additionally, five factors have been recognized as dimensions of professional publishing, which include technical elements, cultural and literary circles and centers, authors and audiences, electronic systems, and distribution and marketing elements. Ultimately, in the regression model, five independent variables were included in the equation due to their significance level being below .05. Conclusion: The findings of this research contribute to enhancing the awareness and understanding of audiences regarding publishing processes. They also assist publishers and industry managers in recognizing successful trends and existing challenges within the field, as well as in formulating supportive policies and strategies for publishing by relevant authorities.

Ali Dorosti, Ali Asghar Alesheikh, Mohammad Sharif

Advancements in navigation and tracking technologies have resulted in a significant increase in movement data within road networks. Analyzing the trajectories of network-constrained moving objects makes a profound contribution to transportation and urban planning. In this context, the trajectory similarity measure enables the discovery of inherent patterns in moving object data. Existing methods for measuring trajectory similarity in network space are relatively slow and neglect the temporal characteristics of trajectories. Moreover, these methods focus on relatively small volumes of data. This study proposes a method that maps trajectories onto a network-based space to overcome these limitations. This mapping considers geographical coordinates, travel time, and the temporal order of trajectory segments in the similarity measure. Spatial similarity is measured using the Jaccard coefficient, quantifying the overlap between trajectory segments in space. Temporal similarity, on the other hand, incorporates time differences, including common trajectory segments, start time variation and trajectory duration. The method is evaluated using real-world taxi trajectory data. The processing time is one-quarter of that required by existing methods in the literature. This improvement allows for spatio-temporal analyses of a large number of trajectories, revealing the underlying behavior of moving objects in network space.

Lina Purbosari

Halaman Sampul dan Daftar Isi

Guangquan Xu, Wenyu Dong, Jun Xing et al.

Cryptojacking is a type of resource embezzlement attack, wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain profits. It has been common since 2017, and in fact, it once became the greatest threat to network security. To better prove the attack ability the harm caused by cryptojacking, this paper proposes a new covert browser-based mining attack model named Delay-CJ, this model was deployed in a simulation environment for evaluation. Based on the general framework of cryptojacking, Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype implementation. The results show that the existing detection methods used for testing may become invalid as result of this model. In view of this situation, to achieve a more general and robust detection scheme, we built a cryptojacking detection system named CJDetector, which is based on cryptojacking process features. Specifically, it identifies malicious mining by monitoring CPU usage and analyzing the function call information. This system not only effectively detects the attack in our example but also has universal applicability. The recognition accuracy of CJDetector reaches 99.33%. Finally, we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real network. We found that although cryptojacking is indeed on the decline, it remains a part of network security threats that cannot be ignored.

Per Fors, Thomas Taro Lennerfors, Jonathan Woodward

This paper aims to outline an approach for case-based chemistry and chemical engineering education for sustainability. Education for Sustainability is assumed to offer a holistic approach to equip students with the knowledge, skills, values, and attitudes needed to contribute to a more sustainable society in their future careers. While Case-Based Education traditionally focuses on disciplinary learning in simulated settings, it can also effectively teach essential sustainability-related skills like integrated problem-solving, critical thinking, and systems thinking. The approach we propose is “case hacking”, which should be understood as utilizing existing business cases while incorporating supplementary resources to align the assignment with intended learning objectives. This expansion of the cases involves, among other things, introducing additional questions and assignments, perspectives from stakeholders previously unexplored in the original case, and the integration of recent research articles from relevant fields. We advocate for the use of case hacking when educators want to harness the educational benefits of Case-Based Education while emphasizing the complexity of sustainability-related challenges faced by industrial companies today. As an illustrative example, we demonstrate the process of hacking a case related to Green Chemistry in the pharmaceutical industry, highlighting specific challenges for chemistry and chemical engineering education. We hope this example will inspire educators in these disciplinary contexts to engage with the case hacking approach as they navigate the complex terrain of sustainability.

Giuseppe Buja, Simone Castellan, Qingsong Wang

Reactive electric spring (RES) is a technique aimed at stabilizing the user voltage in presence of grid voltage variations by means of a user-encapsulated circuit. In spite of the numerous papers on the matter, no expressions are still available to size the RES elements. This paper fills this lack, by drawing up a sizing procedure of them. The procedure starts with a targeted investigation of the RES operation and exploits the outcomes to provide expressions for the values of the passive elements as well as for the voltage-current ratings of the voltage source inverter (VSI). The sizing expressions are formulated in normalized form to emphasize their dependence on the parameters of the user non-critical load. Focus of the sizing procedure is on the two RES key-elements, namely the AC-side capacitor and VSI but the AC filter inductor and the DC-side capacitor are also sized. Two options for sizing the AC-side capacitor are also discussed. At last, the study case of a user supplied by a low-voltage distribution line is considered and the sizing expressions are utilized to calculate the RES data. Experimental results, obtained by an on-purpose arranged hardware-in-the-loop (HIL) rig, validate the sizing procedure.

Meirong ZHANG, Mengting HAO, Chuang WANG et al.

With the vigorous development of space communication technology and the continuous advancement of space-integrated-ground information network, satellite communication networks resource management and control is becoming more and more complex.Due to the scarcity of satellite resources, the slowness of resource scheduling compared to the status refreshing, and uneven distribution of business, eff ciently managing resources has become one of urgent problems to be solved in the development of satellite communications.In view of the heterogeneous network system architecture of high and low orbit satellites, the challenges, its network resource management and control facing, were analyzed.Integrating on the basis of traditional management and control architecture, the collaborative management and control architecture based on group management was introduced.The management strategy of satellite network virtual resource pool was explained to relieved resources scarcity.Resource scheduling algorithms based on deep reinforcement learning(DRL) was introduced to solved the mismatch problem of traditional scheduling methods in complex environments.Beam-hopping technology was adopted to deal with the two-dimensional unevenness of service distribution in time and space.

Géza Kulcsár, Kadosa Koltai, Szvetlin Tanyi et al.

Through the increasing complexity and dynamics of industrial automation scenarios, the notion of Systems of Systems (SoS) has gained importance in this field. Here, interconnected constituent (hardware as well as software) systems collaborate in order to achieve common goals and to increase the efficiency of certain industrial applications.The Arrowhead Tools project aims at proposing a comprehensive, flexible platform for supporting SoS engineering in its every phase, in the form of an engineering toolchain. Thereby, although the SoS operation aspect has been already elaborated on, the design phase and the usage of design artifacts as part of a continous tool interoperability scenario received less attention so far. In this paper, we describe such a toolchain interoperability scenario, paving the way towards an established, integrated solution, by linking systems modeling practices with SoS operational management. In particular, we propose a customtailored abstract SysML profile for Arrowhead SoS design, and a prototype implementation for a bidirectional link between SoS models and a tool for managing Arrowhead SoS via a customtailored textual format. We illustrate the approach through a realistic industrial application.

Aulia Ishak, Poltak Nababan

Production planning has an important role in the company's business processes. A company engaged in the manufacture of intermediate gear parts has a problem in optimizing its production system. The production planning system that occurs is still based on predictions from decision-makers. This study aims to optimize the production planning system to maximize the 15T intermediate gear spare parts' production capacity and the 30T intermediate gear spare parts. Optimization of production planning uses the fuzzy goal programming method to optimize objectives based on existing constraints such as working hours, profit tolerance values, and demand tolerance values. The results showed that the use of fuzzy goal programming was able to increase the production level by 2.765, with an increase in profit of 2.57%. Fuzzy goal programming implementation provides an optimal solution in increasing profits in accordance with company goals based on the constraints that occur.

L. Gitelman, M. Kozhevnikov, O. Ryzhuk

The objectives of this study are to substantiate the principles of advance education and the requirements for the new generation of educational programs aimed at developing competences for the design, manufacture, operation and maintenance of complex industrial systems integrated into intellectual production, environments, and robotic systems. The study included formulating a hypothesis, refining the conceptual framework, creating the necessary empirical base, reviewing the cases of world leading universities, and presenting and substantiating the main features of advance management education that meet the current challenges. A flexible modular architecture of the educational process was developed which allows making timely adjustments to the content and teaching methods to include new knowledge. The experience of testing the results obtained in consulting practice, in teaching master’s degree students at Ural Federal University (Russia) and in educational projects for leaders and breakthrough teams of large energy corporations is described. The academic novelty of the results lies in the comprehensive examination of the issue of advance education, the conditions and the tools for its implementation within the framework of the proactive management methodology developed by the authors for sustainable business development in a revolutionary and changing industrial landscape.