Hasil untuk "Industrial hygiene. Industrial welfare"

Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio

This paper presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied ROS2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available at https://codeocean.com/capsule/8515919/tree/v2.

Rongfei Li, Francis Assadian

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

Matthieu Mastio, Paul Saves, Benoit Gaudou et al.

Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.

Rafael Castro, Alexandre dos Santos Roque

Nowadays, more effective control of hand hygiene (HH) by healthcare teams has become essential. HH control is crucial to prevent cross-contamination and healthcare-associated infections (HAI), according to Brazilian regulatory standards and WHO guidelines. The lack of widespread technology to measure acceptable hygiene rates within hospital environments leads to the practice of a manual sample audit reading, requiring more time for decision making. Thus, the present study addresses the lack of automation technologies for HH, aiming to record, measure, and provide data for internal audits in hospitals. This article introduces an embedded system for HH control and recording, comprising low-cost hardware architecture with IoT connectivity and online monitoring. Results with practical evaluation in a real hospital setting for 3 hours demonstrated the system's effectiveness in recording HH indices.

Meng Li, Yu Zhang, Leshan Li

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles in R&D work-flow. In this paper we discussed the required qualities of industrial designer in the new era, according to an investigation among Chinese enterprises. Additionally, how to promote these qualities though educational program.

Nathan Lane

I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.

Adrián Pérez-Resa, Miguel García-Bosque, Carlos Sánchez-Azqueta et al.

Industrial Ethernet is a technology widely spread in factory floors and critical infrastructures where a high amount of data need to be collected and transported. Fiber optic networks at gigabit rates fit well with that type of environments where speed, system performance and reliability are critical. In this work a new encryption method for high speed optical communications suitable for such kind of networks is proposed. This new encryption method consists of a symmetric streaming encryption of the 8b/10b data flow at PCS (Physical Coding Sublayer) level. It is carried out thanks to an FPE (Format Preserving Encryption) blockcipher working in CTR (Counter) mode. The overall system has been simulated and implemented in an FPGA (Field Programmable Gate Array). Thanks to experimental results it can be concluded that it is possible to cipher traffic at this physical level in a secure way. In addition, no overhead is introduced during encryption, getting minimum latency and maximum throughput.

Wenbo Sui, Daniel Lichau, Josselin Lefèvre et al.

Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.

Zhipeng Ma, Bo Nørregaard Jørgensen, Zheng Grace Ma

Advanced machine learning algorithms are increasingly utilized to provide data-based prediction and decision-making support in Industry 4.0. However, the prediction accuracy achieved by the existing models is insufficient to warrant practical implementation in real-world applications. This is because not all features present in real-world datasets possess a direct relevance to the predictive analysis being conducted. Consequently, the careful incorporation of select features has the potential to yield a substantial positive impact on the outcome. To address the research gap, this paper proposes a novel hybrid framework that combines the feature importance detector - local interpretable model-agnostic explanations (LIME) and the feature interaction detector - neural interaction detection (NID), to improve prediction accuracy. By applying the proposed framework, unnecessary features can be eliminated, and interactions are encoded to generate a more conducive dataset for predictive purposes. Subsequently, the proposed model is deployed to refine the prediction of electricity consumption in foundry processing. The experimental outcomes reveal an augmentation of up to 9.56% in the R2 score, and a diminution of up to 24.05% in the root mean square error.

Ariel Monzón Velasco, Alexis Lorenzo Ruiz, Yudanis González González et al.

Introducción: El estudio de la personalidad de trabajadores y aspirantes es primordial en los procesos de gestión del capital humano en la Unión Eléctrica. Para su evaluación es necesario el empleo de herramientas válidas y confiables que permitan una interpretación precisa, estable y ética de los resultados. Objetivos: Examinar la forma 5 del cuestionario de 16 factores de personalidad de Cattell en trabajadores de la Unión Eléctrica; obtener evidencias de la validez, la confiabilidad de esta prueba y establecer los valores normativos para su calificación en la población de referencia. Métodos: Se desarrolló una investigación cuantitativa, de tipo descriptiva y corte transversal, entre los meses de mayo de 2022 y junio de 2023. Se aplicó un muestreo no probabilístico intencional a 445 trabajadores. Se estudió la consistencia interna del instrumento, su estabilidad, estructura factorial y las puntuaciones descriptivas de los factores de primer orden. Resultados: Los resultados muestran que el instrumento cuenta con una consistencia interna adecuada y su estructura factorial se ajusta a las planteadas por los manuales de la prueba para las versiones en inglés y español. Se establecieron los valores normativos para la calificación del instrumento en la población de referencia. Conclusiones: La investigación examinó las propiedades psicométricas y las puntuaciones fundamentales del instrumento; permitió la elaboración de sus normas de calificación para la población de referencia y deja una serie de recomendaciones para hacer más válidas y confiables la utilización de los cuestionarios de personalidad de Cattell en la Unión Eléctrica Introduction: The study of the personality of workers and applicants is essential in the processes of human capital management in the Electric Union. For its evaluation, it is necessary to use valid and reliable tools that allow an accurate, stable and ethical interpretation of the results. Objectives: To examine Form 5 of the Cattell´s questionnaire of 16 personality factors in workers of the Electric Union; to collect evidence of the validity and reliability of this test and to establish the normative values for its qualification in the reference population. Methods: A quantitative, descriptive and cross-sectional research was developed between May 2022 and June 2023. Intentional non-probability sampling was applied to 445 workers. The internal consistency of the instrument, its stability, factorial structure and the descriptive scores of the first-order factors were studied. Results: The results show that the instrument has an adequate internal consistency and its factorial structure conforms to those proposed by the test manuals for the English and Spanish versions. The normative values for the qualification of the instrument in the reference population were established. Conclusions: The research examined the psychometric properties and fundamental scores of the instrument; allowed the elaboration of its qualification standards for the reference population and leaves a series of recommendations to make the use of Cattell's personality questionnaires in the Electric Union more valid and reliable

Marc Schmitt

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

Miki Tanaka, Tomoaki Okuda, Kouichi Itoh et al.

Abstract Background A recent epidemiological study showed that air pollution is closely involved in the prognosis of ischemic stroke. We and others have reported that microglial activation in ischemic stroke plays an important role in neuronal damage. In this study, we investigated the effects of urban aerosol exposure on neuroinflammation and the prognosis of ischemic stroke using a mouse photothrombotic model. Results When mice were intranasally exposed to CRM28, urban aerosols collected in Beijing, China, for 7 days, microglial activation was observed in the olfactory bulb and cerebral cortex. Mice exposed to CRM28 showed increased microglial activity and exacerbation of movement disorder after ischemic stroke induction. Administration of core particles stripped of attached chemicals from CRM28 by washing showed less microglial activation and suppression of movement disorder compared with CRM28-treated groups. CRM28 exposure did not affect the prognosis of ischemic stroke in null mice for aryl hydrocarbon receptor, a polycyclic aromatic hydrocarbon (PAH) receptor. Exposure to PM2.5 collected at Yokohama, Japan also exacerbated movement disorder after ischemic stroke. Conclusion Particle matter in the air is involved in neuroinflammation and aggravation of the prognosis of ischemic stroke; furthermore, PAHs in the particle matter could be responsible for the prognosis exacerbation.

Kurnia Ardiansyah Akbar, Reny Indrayani, Adinda Jasmine Rohmaniah

Introduction: The increasing number of Covid-19 cases in Indonesia triggers fear and anxiety in the community and causes stress. Teachers who work are at risk of experiencing job stress. The change in the learning system from face-to-face to distance learning requires teachers to adapt to the new technology applied. The purpose of this research was to analyze the relationship between the understanding of the Google Classroom application and duration of work during the Covid-19 pandemic with job stress. Methods: This research is an analytical research with a cross sectional approach. The research was conducted in 8 public high schools in Nganjuk District, and the samples were 115 teachers. Data retrieval was done by using an online questionnaire via Google Form. The variables in this research were the understanding of the Google Classroom (knowledge, perceptions of the usefulness and ease of use), work duration and job stress. Bivariate analysis used Spearmans’ Rho correlation test to determine the relationship between variables. Results: Most respondents had a moderate level of knowledge, and most of them had a fairly good perception of the usefulness and ease of use of the Google Classroom application. The duration of work that most respondents had was 2 hours, and the highest category of job stress was mild stress. Conclusion: The understanding of the Google Classroom application (knowledge of the Google Classroom application, the perception of the usefulness of the Google Classroom application and the perception of the ease of use of the Google Classroom application) and the duration of work had a significant relationship with job stress among public high school teachers in Nganjuk District during the Covid-19 pandemic.

Rani Marfuah, Endah Dwi Handayani

Introduction: Physical factors found in the workplace can have an impact on occupational health and safety; one example of these physical factors is high noise intensity. One of the workplaces that have high noise intensity is a steel manufacturing company. The purpose of this study is to determine the noise risk based on noise mapping and analyse efforts that have been made in the Continous Tandem Cold Mill area in a steel company in Cilegon. Methods: The method used in this study was descriptive method. The variables used were the results of noise intensity measurements. The data were collected by means of literature study, field observation and noise measurements. The data obtained were then analysed using a descriptive method and were used as a basis in developing noise mapping. Results: Based on noise mapping, the welder area has the highest noise intensity of 91.1 - 94 dBA. Efforts to control noise intensity that have been carried out in the company are administrative control and personal protective equipment. Conclusion:From the results of noise intensity measurements in the Continous Tandem Cold Mill area of a steel company in Cilegon, it can be concluded that the measurement point is 76% - 100% and that the noise measurement points exceed the threshold value stipulated in the Regulation of the Minister of Manpower of the Republic Indonesia Number 5 of 2018. However, the steel company in Cilegon has made several efforts to reduce the noise intensity. Keywords: noise, noise mapping, steel company

Shihui Han, Roopak Sinha, Andrew Lowe

Industrial standards for developing medical device software provide requirements that conforming devices must meet. A number of reference software architectures have been proposed to develop such software. The ISO/IEC 25010:2011 family of standards provides a comprehensive software product quality model, including characteristics that are highly desirable in medical devices. Furthermore, frameworks like 4+1 Views provide a robust framework to develop the software architecture or high level design for any software, including for medical devices. However, the alignment between industrial standards and reference architectures for medical device software, on one hand, and ISO/IEC 25010:2011 and 4+1 Views, on the other, is not well understood. This paper aims to explore how ISO/IEC 25010:2011 and 4+1 Views are supported by current standards, namely ISO 13485:2016, ISO 14971:2012, IEC 62304:2006 and IEC 62366:2015, and current reference architectures for medical device software. We classified requirements from medical devices standards into qualities from ISO/IEC 25010:2011 and architectural views from 4+1 Views. A systematic literature review (SLR) method was followed to review current references software architectures and a mapping of their support for the identified ISO/IEC 25010:2011 qualities in the previous step was carried out. Our results show that ISO/IEC 25010:2011 qualities like functional suitability, portability, maintainability, usability, security, reliability and compatibility are highly emphasised in medical device standards. Furthermore, we show that current reference architectures only partially support these qualities. This paper can help medical device developers identify focus areas for developing standards-compliant software. A wider study involving under-development medical devices can help improve the accuracy of our findings in the future.

Barry Dowdeswell, Roopak Sinha, Stephen G. MacDonell

Integrating the design and creation of fault identification and diagnostic capabilities into Model-Driven Development methodologies is one approach to enhancing the resilience of Industrial Cyber-Physical Systems. We present a Fault Diagnostic Engine designed to recognise and diagnose faults in IEC 61499 Function Block Applications. Using diagnostic agents that interact directly with the target application, we demonstrate fault monitoring and analysis techniques and as well as failure scenario intervention. By designing and building fault diagnostic resources during early phases of Model-Driven Development, both iterative testing and long-term fault management capabilities can be created. While applying and refining appropriate model artifacts, we demonstrate that the concurrent development of function blocks alongside fault management capabilities is both feasible and worthwhile.

Awais Tanveer, Roopak Sinha, Stephen G. MacDonell et al.

Programmable Logic Controllers (PLCs) execute critical control software that drives Industrial Automation and Control Systems (IACS). PLCs can become easy targets for cyber-adversaries as they are resource-constrained and are usually built using legacy, less-capable security measures. Security attacks can significantly affect system availability, which is an essential requirement for IACS. We propose a method to make PLC applications more security-aware. Based on the well-known IEC 61499 function blocks standard for developing IACS software, our method allows designers to annotate critical parts of an application during design time. On deployment, these parts of the application are automatically secured using appropriate security mechanisms to detect and prevent attacks. We present a summary of availability attacks on distributed IACS applications that can be mitigated by our proposed method. Security mechanisms are achieved using IEC 61499 Service-Interface Function Blocks (SIFBs) embedding Intrusion Detection and Prevention System (IDPS), added to the application at compile time. This method is more amenable to providing active security protection from attacks on previously unknown (zero-day) vulnerabilities. We test our solution on an IEC 61499 application executing on Wago PFC200 PLCs. Experiments show that we can successfully log and prevent attacks at the application level as well as help the application to gracefully degrade into safe mode, subsequently improving availability.

Filipe M. de Brito, Gélson da Cruz Júnior, Enzo M. Frazzon et al.

In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges, leading to new difficulties for SC design. In this context, this paper proposes a new design approach to AM SCs using optimization methods. More specifically, the proposed approach, comprising the p-median and mixed-integer linear programming models, considers the decision of deploying productive resources (3D printers) in specific locations of generic spare part SCs. The approach was evaluated in a real-world use case of an elevator maintenance service provider. The obtained results demonstrated the promising capabilities of the proposed design approach in managing the challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs.

Alessandro Erba, Nils Ole Tippenhauer

In recent years, a number of process-based anomaly detection schemes for Industrial Control Systems were proposed. In this work, we provide the first systematic analysis of such schemes, and introduce a taxonomy of properties that are verified by those detection systems. We then present a novel general framework to generate adversarial spoofing signals that violate physical properties of the system, and use the framework to analyze four anomaly detectors published at top security conferences. We find that three of those detectors are susceptible to a number of adversarial manipulations (e.g., spoofing with precomputed patterns), which we call Synthetic Sensor Spoofing and one is resilient against our attacks. We investigate the root of its resilience and demonstrate that it comes from the properties that we introduced. Our attacks reduce the Recall (True Positive Rate) of the attacked schemes making them not able to correctly detect anomalies. Thus, the vulnerabilities we discovered in the anomaly detectors show that (despite an original good detection performance), those detectors are not able to reliably learn physical properties of the system. Even attacks that prior work was expected to be resilient against (based on verified properties) were found to be successful. We argue that our findings demonstrate the need for both more complete attacks in datasets, and more critical analysis of process-based anomaly detectors. We plan to release our implementation as open-source, together with an extension of two public datasets with a set of Synthetic Sensor Spoofing attacks as generated by our framework.

Jin Quan Zhou, Wen Jin He

The industrial life cycle theory has proved to be helpful for describing the evolution of industries from birth to maturity. This paper is to highlight the historical evolution stage of Atlantic City's gambling industry in a structural framework covered by industrial market, industrial organization, industrial policies and innovation. Data mining was employed to obtain from local official documents, to verify the module of industrial life cycle in differential phases as introduction, development, maturity and decline. The trajectory of Atlantic City's gambling sector evolution reveals the process from the stages of introduction to decline via a set of variables describing structural properties of this industry such as product, market and organization of industry under a special industry environment in which industry recession as a result of theory of industry life cycle is a particular evidence be proved again. Innovation of the gambling industry presents the ongoing recovering process of the Atlantic City gambling industry enriches the theory of industrial life cycle in service sectors.