Hasil untuk "Industry"

Jay Lee, Hanqi Su

Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

Weizhi Zhang, Liangwei Yang, Wooseong Yang et al.

Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with cold-start and data-sparse scenarios. Recent advancements in pre-trained large language models (LLMs) with rich semantic knowledge, offer promising solutions to these challenges. However, deploying LLMs at scale is hindered by their significant computational demands and latency. In this paper, we propose a novel and scalable LLM-RecSys framework, LLMInit, designed to integrate pretrained LLM embeddings into CF models through selective initialization strategies. Specifically, we identify the embedding collapse issue observed when CF models scale and match the large embedding sizes in LLMs and avoid the problem by introducing efficient sampling methods, including, random, uniform, and variance-based selections. Comprehensive experiments conducted on multiple real-world datasets demonstrate that LLMInit significantly improves recommendation performance while maintaining low computational costs, offering a practical and scalable solution for industrial applications. To facilitate industry adoption and promote future research, we provide open-source access to our implementation at https://github.com/DavidZWZ/LLMInit.

Joseph Bak-Coleman, Cailin O'Connor, Carl Bergstrom et al.

Emerging information technologies like social media, search engines, and AI can have a broad impact on public health, political institutions, social dynamics, and the natural world. It is critical to develop a scientific understanding of these impacts to inform evidence-based technology policy that minimizes harm and maximizes benefits. Unlike most other global-scale scientific challenges, however, the data necessary for scientific progress are generated and controlled by the same industry that might be subject to evidence-based regulation. Moreover, technology companies historically have been, and continue to be, a major source of funding for this field. These asymmetries in information and funding raise significant concerns about the potential for undue industry influence on the scientific record. In this Perspective, we explore how technology companies can influence our scientific understanding of their products. We argue that science faces unique challenges in the context of technology research that will require strengthening existing safeguards and constructing wholly new ones.

Devi Yulia Rahmi, Fikri Alwi, Ratni Prima Lita et al.

Objective: The study aims to examine the effect of halal awareness, halal logo, religiosity, and price on consumers' intention to buy food products with a halal logo. This study also examines the effect of purchase intention on consumer purchase behaviour of food products with the halal logo Research Design & Methods: This study uses a quantitative method with 200 respondents who consume food products with the halal logo in West Sumatra. Data were analyzed using the PLS-SEM (Partial Least Square-Structural Equational Modeling) method. Findings: The results of the study show that the halal logo, religiosity, and price are significant for purchase intention. No significant effect was found on halal awareness and attitude towards purchase intention. Moreover, the study's results show that purchase intention significantly affects purchase behaviour. Implications and Recommendations: This research implies that consumers of food products labelled halal consider the halal logo on a product before consuming it, so food producers must try to sell products that have a halal logo Contribution & Value Added: This study addresses the existing literature by modifying the research model regarding the purchase behaviour of halal products. In practical, companies in the halal food industry should focus on maintaining and enhancing product quality, obtaining halal certification, and establishing a trusted halal label to foster consumer purchase intention and behavior.

Sascha Meckler

Enterprise knowledge graphs combine business data and organizational knowledge by means of a semantic network of concepts, properties, individuals and relationships. The graph-based integration of previously unconnected information with domain knowledge provides new insights and enables intelligent business applications. However, knowledge graph construction is a large investment which requires a joint effort of domain and technical experts. This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case. The self-contained process adapts the "Cross Industry Standard Process for Data Mining" and uses competency questions throughout the entire development cycle. The procedure model starts with business and data understanding, describes tasks for ontology modeling and the graph setup, and ends with process steps for evaluation and deployment.

F. Adetunji, A. Karukayil, P. Samant et al.

This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.

Mohammed Abdulsalam Alsofiani

Building Information Modeling (BIM) is a crucial technology in the construction industry, offering benefits such as enhanced collaboration, real-time decision-making, and significant cost and time savings. Despite its advantages, BIM adoption faces numerous barriers. This study aims to create a reliable tool to assess the Rate of BIM Adoption (RBA), drawing on Attributes of Innovation theory and empirical data from the literature. This research integrates theoretical insights with empirical data, providing quantitative items to measure BAR in the construction industry. The quantitative approach helps decision-makers and policymakers to mandate BIM and establish appropriate implementation standards. Its implications are significant for the construction industry, policymakers, and the academic community, offering a systematic approach to assess BIM adoption, identify barriers, and implement targeted strategies. The reliability of this approach is ensured through a solid theoretical foundation, item development, pilot testing, and statistical analysis, making it a valuable resource for improving BIM implementation and fostering innovation in the construction industry.

Xuejiao Li, Cheng Yang, Charles Møller et al.

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.

Kristin A. Oliver, Victoria Borish, Bethany R. Wilcox et al.

As quantum technologies transition out of the research lab and into commercial applications, it becomes important to better prepare students to enter this new and evolving workforce. To work towards this goal of preparing physics students for a career in the quantum industry, a senior capstone course called "Quantum Forge" was created at the University of Colorado Boulder. This course aims to provide students a hands-on quantum experience and prepare them to enter the quantum workforce directly after their undergraduate studies. Some of the course's goals are to have students understand what comprises the quantum industry and have them feel confident they could enter the industry if desired. To understand to what extent these goals are achieved, we followed the first cohort of Quantum Forge students through their year in the course in order to understand their perceptions of the quantum industry including what it is, whether they feel that they could be successful in it, and whether or not they want to participate in it. The results of this work can assist educators in optimizing the design of future quantum-industry-focused courses and programs to better prepare students to be a part of this burgeoning industry.

Haoyuan Jiang, Xuantang Xiong, Ziyue Li et al.

Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limited than simulation-based RL methods. For real-world solutions, only flow can be reliably collected, whereas common RL methods need more. For the output action, most RL methods focus on acyclic control, which real-world signal controllers do not support. Most importantly, industry standards require a consistent cycle-flow relationship: non-decreasing and different response strategies for low, medium, and high-level flows, which is ignored by the RL methods. To narrow the gap between RL methods and industry standards, we innovatively propose to use industry solutions to guide the RL agent. Specifically, we design behavior cloning and curriculum learning to guide the agent to mimic and meet industry requirements and, at the same time, leverage the power of exploration and exploitation in RL for better performance. We theoretically prove that such guidance can largely decrease the sample complexity to polynomials in the horizon when searching for an optimal policy. Our rigid experiments show that our method has good cycle-flow relation and superior performance.

Dániel Gosztola, Peter Grubits, János Szép et al.

The growing importance of numerical simulations in the welding industry stems from their ability to enhance structural performance and sustainability by ensuring optimal manufacturing conditions. The use of the finite element method (FEM) allows for detailed and precise calculations of the mechanical and material changes caused by the welding process. Acquiring knowledge of these parameters not only serves to augment the quality of the manufacturing process but also yields consequential benefits, such as reducing adverse effects. Consequently, the enhancement of structural performance and prolonged lifespan becomes achievable, aligning with overarching sustainability goals. To achieve this goal, this paper utilizes numerical simulations of welding processes based on experimental tests, with a specific focus on analyzing temperatures generated within the structures. In the finite element analysis (FEA), a total of 12 welding cycles were systematically modeled to align with experimental conditions, incorporating cooling intervals, preheating considerations, and the relevant section of the connecting concrete structure with studs. The outcomes of this research exemplify the potential of numerical simulation in the welding industry, demonstrating a diverse range of results achieved through FEA to enhance the quality of structures within the context of sustainability.

Engin Derya Gezer, Abdullah Uğur Birinci, Aydın Demir et al.

The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN) prediction models. Subsequently, using the models obtained from the analyses, the goal was to identify the optimum layer combinations of timber strength classes used in the middle and outer layers that would provide the highest mechanical properties for CLT panels. CLT panels made from spruce and alder timbers, as well as hybrid panels created from combinations of these two wood species, were produced. The strength classes of the timbers were determined non-destructively according to the TS EN 338 (2016) standard using an acoustic testing device. The bending strength and modulus of elasticity values of the CLT panels were determined destructively according to the TS EN 408 (2019) standard. According to ANN results, the optimum timber strength classes and layer combinations were determined for bending strength as C24-C27-C24 for spruce CLT, D18-D24-D18 for alder CLT, C30-D40-C30 and D18-C30-D18 for hybrid panels; and for modulus of elasticity, C22-C27-C22 for spruce, D35-D30-D35 for alder, C16-D24-C16, and D24-C24-D24 for hybrid panels.

FENG Jianghua, TAN Bo, DOU Zechun et al.

With the continuous advancement of China's "dual carbon" goals and the ongoing optimization of the energy mix, the electrification of transportation equipment, as a low-carbon and environmentally-friendly approach, has become an important development trend in the transportation industry. This paper presents the exploration in high-power-density electrification technologies for transportation equipment, focusing on those in the chain-type key technology routes encompassing devices, components, equipment, systems and architectures. Taking products supplied by CRRC Zhuzhou Institute Co., Ltd. as an case study, detailed investigations were made into five key technologies for high-power-density design: high-frequency converters, customized devices, silicon-based equipment, structural integration, and diversified networking, and three high-power-density generic technologies: thermal management, electromagnetic compatibility, and reliability, highlighting their key roles in improving the performance, efficiency, and reliability of transportation equipment. The study summarizes the current research status concerning the development of transportation equipment towards higher power, lighter weight, and smaller size. For future development in high-power-density electrification technologies, this paper suggests a focus on continuous innovation and development in four areas: the innovation chain, intelligent systems, new power semiconductor device technologies, and safety. The research outcomes provide strong support for the green transformation and sustainable development of the transportation industry.

Tatalina Oliveira, Ann Barcomb, Ronnie de Souza Santos et al.

Context. Women remain significantly underrepresented in software engineering, leading to a lasting gender gap in the software industry. This disparity starts in education and extends into the industry, causing challenges such as hostile work environments and unequal opportunities. Addressing these issues is crucial for fostering an inclusive and diverse software engineering workforce. Aim. This study aims to enhance the literature on women in software engineering, exploring their journey from academia to industry and discussing perspectives, challenges, and support. We focus on Brazilian women to extend existing research, which has largely focused on North American and European contexts. Method. In this study, we conducted a cross-sectional survey, collecting both quantitative and qualitative data, focusing on women's experiences in software engineering to explore their journey from university to the software industry. Findings. Our findings highlight persistent challenges faced by women in software engineering, including gender bias, harassment, work-life imbalance, undervaluation, low sense of belonging, and impostor syndrome. These difficulties commonly emerge from university experiences and continue to affect women throughout their entire careers. Conclusion. In summary, our study identifies systemic challenges in women's software engineering journey, emphasizing the need for organizational commitment to address these issues. We provide actionable recommendations for practitioners.

Sung Youn Boo, Steffen Allan Shelley, Seung-Ho Shin et al.

There has been growing interest recently in hybrid installations integrating the offshore wind farm and aquaculture farm as co-existence while optimizing ocean space use. The offshore marine farms beyond coastal or sheltered areas will require mooring to ensure the station-keeping of the farm system during the storms. In the present work, a sub-surface longline farm is installed in a fixed offshore wind farm at a distance from the wind foundations. The farm is designed to cultivate oysters in multi-compartment bags attached to the longlines vertically. The farm with a cultivating area of 200 m × 200 m is supported by the various farm lines made of polypropylene and buoys that is moored with catenary mooring arrangements. Drag coefficients of a full-scale oyster bag in wave and current are determined using the results of wave basin tests. A lumped model is developed and validated with a complete model for a partial farm. The lumped model is used to simulate the coupled responses of the whole farm in the site extreme waves and currents of a 50-year return period. The strength and fatigue designs of the mooring and farm lines are evaluated against the industry standards and confirmed to comply with the design requirements.

Serena Lima, Antonino Biundo, Giuseppe Caputo et al.

As known, microalgae are an appealing source of chemicals and high-value compounds which find application in nutraceuticals, cosmetics and pharmaceutics. Fatty acids (FA), in particular, have drawn attention to the possibility of employing them as a source of biodiesel alternatively to fossil fuels. In addition, several lipid derivatives have been found in microalgae and may be employed in several biotechnological applications. Hydroxy fatty acids can be substrates for several industrial applications thanks to their functionalization, which increases their reactivity and, for this reason, can be used as functional building blocks to produce a multitude of bio-based materials. Recently, a promising method for the chemical modification of unsaturated-FAs (U-FA) has appeared. In fact, U-FA may be modified by members of the hydratase enzyme family to produce saturated and unsaturated hydroxy fatty acids with high stereo- and regio-selectivity. These enzymes are able to introduce a water molecule to the double bond present in the free fatty acids (FFA) Oleic Acid (OA), Linoleic Acid (LA), producing 10-hydroxy fatty acids (10-hydroxy-FAs). Furthermore, the carbohydrate component of the microalgal biomass may be converted into furfuryl compounds and, in particular in 5-hydroxyl methyl furfural (5-HMF). This is one of the chemical bio-compound different from petroleum-derived ones with the highest added value and may be obtained through lignocellulosic biomasses or hexoses sugars through acid catalysis. It is defined platform molecule because it is the precursor of several compounds for the chemical industry. In this work, we aimed to optimize a circular bioprocess by performing, starting from the same biomass, two different processes: the biotransformation of microalgal FFAs through the employment of a genetically modified E. coli on one side, and the conversion of the remaining biomass in furfuryl products. The first process allowed the production of very interesting lipid derivatives with biotechnological applications, including 10 hydroxy-stearic acid and 10-hydroxy-octadecenoic acid. The second process was obtained through heterogeneous catalysis based on niobium phosphate. This procedure represents a high-innovative application of microalgal biomass and allows the simultaneous exploitation of FAs and carbohydrates. This may result in an increase in the commercial value of microalgal biomass.

James Ivers, Robert L. Nord, Ipek Ozkaya et al.

Software refactoring plays an important role in software engineering. Developers often turn to refactoring when they want to restructure software to improve its quality without changing its external behavior. Studies show that small-scale (floss) refactoring is common in industry and can often be performed by a single developer in short sessions, even though developers do much of this work manually instead of using refactoring tools. However, some refactoring efforts are much larger in scale, requiring entire teams and months of effort, and the role of tools in these efforts is not as well studied. In this paper, we report on a survey we conducted with developers to understand large-scale refactoring, its prevalence, and how tools support it. Our results from 107 industry developers demonstrate that projects commonly go through multiple large-scale refactorings, each of which requires considerable effort. While there is often a desire to refactor, other business concerns such as developing new features often take higher priority. Our study finds that developers use several categories of tools to support large-scale refactoring and rely more heavily on general-purpose tools like IDEs than on tools designed specifically to support refactoring. Tool support varies across the different activities, with some particularly challenging activities seeing little use of tools in practice. Our study demonstrates a clear need for better large-scale refactoring tools and an opportunity for refactoring researchers to make a difference in industry. The results we summarize in this paper is one concrete step towards this goal.

Quan Quan, Zhongqiang Zhang

With the rapid development of economy and information technology, traditional manufacturing industry is facing severe challenges. Enterprises need to rectify the traditional manufacturing industry and realize the transformation from traditional manufacturing industry to intelligent manufacturing industry. In order to adapt to market demand, enterprises need to constantly integrate resources to improve the competitiveness of enterprise supply chain. Based on the background of suppliers in intelligent manufacturing enterprises, the evaluation method of supplier efficiency was studied by using machine learning. In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO-BP neural network is constructed. Through the collected sample data, the network is trained and simulated, and the results are analyzed. Finally, the designed model is applied to a large battery manufacturing enterprise, and the supplier efficiency evaluation method based on DPMPSO-BP neural network is validated and analyzed. Compared with the traditional BP neural network method, the supplier efficiency evaluation method is effective and feasible.

Ji Liu, Faying Qiang, Ying Zhou

The Covid‐19 pandemic has catalyzed irreversible structural changes in education systems worldwide. One key development is the broad utility of remote digital e‐learning modalities for learning and instruction that could jeopardize social inclusion if digital in(ex)clusion is left unaddressed. This study assembles a two‐step mixed method research design and conducts a case inquiry of Shaanxi Province in China by leveraging policy document analysis and rapid survey methodology in examining how transitions to remote digital e‐learning may introduce learning barriers to children from vulnerable backgrounds. Findings reveal that children’s access to remote digital e‐learning devices during the rapid transition to e‐learning has a close association with their backgrounds. Key policy implications include utilizing multimodal hybrid technology in diversifying content delivery and maximizing e‐learning coverage, developing open learning platforms, expanding access to e‐learning resources, and collaborating with industry partners to bring tangible support to families and realize meaningful e‐learning at home.