Hasil untuk "Industrial directories"

Chen Fanliang, Sun Ying, Xiao Junhua

Landscape environments present substantial difficulties for autonomous systems because of issues like vegetation occlusion, rolling terrain, varying light levels, and confusing textures that hinder accurate 3D perception and lead path planners to settle upon local optima or to run slowly. To attack this problem, this paper drinks a stride towards proposing the Landscape Perception-Planning Framework (LPPF), an end-to-end lightweight architecture capable of optimizing perception and planning jointly. LPPF includes a MobileNetV3–Swin Transformer architecture integrated to provide robust monocular depth estimation, construction of StyleGAN2-ADA generated synthetic 3D point clouds in multiple weather conditions for the purposes of generalization, and Proximal Policy Optimization (PPO) planner that dynamically adjusts depth confidence into a cost map for error-aware navigation. LPPF is evaluated using 10,000 synthetic LiDAR frames and 500 real LiDAR frames, achieving an overall score of 0.93, an improvement of 19.2% over DPT using the LPPF framework to process under a 50 ms real-time constraint on an embedded platform. By applying channel pruning and INT8 quantization, the model reduces parameters by 85.2% and increases inference by a factor of 3.21 indicating strong accuracy, robustness, and efficiency for intelligent navigation in complex, resource-constrained landscape environments.

Giovanni De Gasperis, Sante Dino Facchini

Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial intelligence. This study presents a comparison between these two methodologies, analyzing their respective strengths, limitations, and application scenarios, and proposes a basic framework to evaluate their key properties. Rule-based systems offer high interpretability, deterministic behavior, and ease of implementation in stable environments, making them ideal for regulated industries and safety-critical applications. However, they face challenges with scalability, adaptability, and performance in complex or evolving contexts. Conversely, data-driven systems excel in detecting hidden anomalies, enabling predictive maintenance and dynamic adaptation to new conditions. Despite their high accuracy, these models face challenges related to data availability, explainability, and integration complexity. The paper suggests hybrid solutions as a possible promising direction, combining the transparency of rule-based logic with the analytical power of machine learning. Our hypothesis is that the future of industrial monitoring lies in intelligent, synergic systems that leverage both expert knowledge and data-driven insights. This dual approach enhances resilience, operational efficiency, and trust, paving the way for smarter and more flexible industrial environments.

Eneko Osaba, Iñigo Perez Delgado, Alejandro Mata Ali et al.

This article explores the current state and future prospects of quantum computing in industrial environments. Firstly, it describes three main paradigms in this field of knowledge: gate-based quantum computers, quantum annealers, and tensor networks. The article also examines specific industrial applications, such as bin packing, job shop scheduling, and route planning for robots and vehicles. These applications demonstrate the potential of quantum computing to solve complex problems in the industry. The article concludes by presenting a vision of the directions the field will take in the coming years, also discussing the current limitations of quantum technology. Despite these limitations, quantum computing is emerging as a powerful tool to address industrial challenges in the future.

Ali Raza, Muhammad Farhan Khan, Zeeshan Alam et al.

This paper presents a joint framework that integrates reconfigurable intelligent surfaces (RISs) with Terahertz (THz) communications and non-orthogonal multiple access (NOMA) to enhance smart industrial communications. The proposed system leverages the advantages of RIS and THz bands to improve spectral efficiency, coverage, and reliability key requirements for industrial automation and real-time communications in future 6G networks and beyond. Within this framework, two power allocation strategies are investigated: the first optimally distributes power between near and far industrial nodes, and the second prioritizes network demands to enhance system performance further. A performance evaluation is conducted to compare the sum rate and outage probability against a fixed power allocation scheme. Our scheme achieves up to a 23% sum rate gain over fixed PA at 30 dBm. Simulation results validate the theoretical analysis, demonstrating the effectiveness and robustness of the RIS-assisted NOMA MIMO framework for THz enabled industrial communications.

Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula et al.

The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on servo motors and a microcontroller interface with Mechanical links were also fabricated. Kinematic and dynamic analyses have been done in order to provide precise positioning and effective loads. Inverse Kinematics algorithm and Proportional-Integral-Derivative (PID) controller were also applied to improve the precision of control. The ability of the system to carry out tasks with high accuracy and repeatability is confirmed by simulation and experimental testing. The suggested robotic arm is an affordable, expandable, and dependable method of automation of numerous mundane procedures in the manufacturing industry.

Sri Handayani, Syarifah Nuraini, Yunita Fiitrianti et al.

Introduction: Since the first case of COVID-19 was detected in Indonesia, the government has implemented Large-Scale Social Restrictions to control the spread of the disease. However, these restrictions have had a negative impact on the economy. To address this, the government has introduced a new normal policy to restore activities while managing the risk of transmission. The government has adopted WHO guidelines through Minister of Health Decree No. 238 of 2020 to ensure COVID-19 workplace safety. This article aims to explore the barriers to COVID-19 workplace safety among Indonesian office workers. Methods: This qualitative research was conducted in DKI Jakarta and Surabaya from September to October 2020. In-depth interviews and observations were conducted with 22 informants selected purposefully. Thematic analysis was used, drawing on the Social-ecological Model (SEM) theory. Results: At the intrapersonal level, fear and perception barriers impact preventive actions against COVID-19. At the interpersonal level, peer influence and perceptions of the work environment affect adherence to office policies. At the organizational level, employee behavior is influenced by socializing, rules, and workplace amenities. Lastly, public policy enforcement is vital at the macro level to reduce risky behaviors among office workers. Conclusion: Implementing comprehensive protocols across different levels is crucial to creating COVID-19 workplace safety. This requires increased public awareness and consistent enforcement, including strengthening organizational policies.

Motoko Ohira, Yoko Ichikawa, Madoka Tsuji et al.

Objectives: Teleworking is a flexible means of working to effectively utilize one’s time and workplace using information and communication technology. However, teleworking can also lead to work–life conflict and health problems. To support the health of teleworkers, this study aimed to elucidate the factors correlated with the self-rated health of teleworkers raising children, focusing on differences between genders. Methods: The study sample included 1,000 teleworkers (500 women and 500 men). Results: The responses to questionnaire items about health differed between men and women. For men, “marital status,” “walks and exercises,” “keeps an uplifted state of mind as much as possible,” and “work-to-family negative spillover” were extracted. For women, “leads a disciplined life,” “keeps an uplifted state of mind as much as possible,” “eating speed compared with others: slower,” and “sufficiently rests through sleep” were found to affect self-rated health. Conclusions: For male teleworkers raising children, sufficient exercise and physical activity is a crucial aspect of health management. For female teleworkers raising children, self-discipline is needed.

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

The growing connectivity of industrial devices as a result of the Internet of Things is increasing the risks to Industrial Control Systems. Since attacks on such devices can also cause damage to people and machines, they must be properly secured. Therefore, a threat analysis is required in order to identify weaknesses and thus mitigate the risk. In this paper, we present a systematic and holistic procedure for analyzing the attack surface and threats of Industrial Internet of Things devices. Our approach is to consider all components including hardware, software and data, assets, threats and attacks throughout the entire product life cycle.

Zongjie Li, Wenying Qiu, Pingchuan Ma et al.

Recent years have witnessed the rapid development of large language models (LLMs) in various domains. To better serve the large number of Chinese users, many commercial vendors in China have adopted localization strategies, training and providing local LLMs specifically customized for Chinese users. Furthermore, looking ahead, one of the key future applications of LLMs will be practical deployment in industrial production by enterprises and users in those sectors. However, the accuracy and robustness of LLMs in industrial scenarios have not been well studied. In this paper, we present a comprehensive empirical study on the accuracy and robustness of LLMs in the context of the Chinese industrial production area. We manually collected 1,200 domain-specific problems from 8 different industrial sectors to evaluate LLM accuracy. Furthermore, we designed a metamorphic testing framework containing four industrial-specific stability categories with eight abilities, totaling 13,631 questions with variants to evaluate LLM robustness. In total, we evaluated 9 different LLMs developed by Chinese vendors, as well as four different LLMs developed by global vendors. Our major findings include: (1) Current LLMs exhibit low accuracy in Chinese industrial contexts, with all LLMs scoring less than 0.6. (2) The robustness scores vary across industrial sectors, and local LLMs overall perform worse than global ones. (3) LLM robustness differs significantly across abilities. Global LLMs are more robust under logical-related variants, while advanced local LLMs perform better on problems related to understanding Chinese industrial terminology. Our study results provide valuable guidance for understanding and promoting the industrial domain capabilities of LLMs from both development and industrial enterprise perspectives. The results further motivate possible research directions and tooling support.

Davide Frizzo, Francesco Borsatti, Alessio Arcudi et al.

Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial application of ExIFFI, a recent approach for fast, efficient explanations for the Extended Isolation Forest (EIF) AD method. ExIFFI is tested on four industrial datasets, demonstrating superior explanation effectiveness, computational efficiency and improved raw anomaly detection performances. ExIFFI reaches over then 90\% of average precision on all the benchmarks considered in the study and overperforms state-of-the-art Explainable Artificial Intelligence (XAI) approaches in terms of the feature selection proxy task metric which was specifically introduced to quantitatively evaluate model explanations.

Gurjot Singh Bhatia, Yoann Corre, Linus Thrybom et al.

Appropriate channel models tailored to the specific needs of industrial environments are crucial for the 5G private industrial network design and guiding deployment strategies. This paper scrutinizes the applicability of 3GPP's channel model for industrial scenarios. The challenges in accurately modeling industrial channels are addressed, and a refinement strategy is proposed employing a ray-tracing (RT) based channel model calibrated with continuous-wave received power measurements collected in a manufacturing facility in Sweden. The calibration helps the RT model achieve a root mean square error (RMSE) and standard deviation of less than 7 dB. The 3GPP and the calibrated RT model are statistically compared with the measurements, and the coverage maps of both models are also analyzed. The calibrated RT model is used to simulate the network deployment in the factory to satisfy the reference signal received power (RSRP) requirement. The deployment performance is compared with the prediction from the 3GPP model in terms of the RSRP coverage map and coverage rate. Evaluation of deployment performance provides crucial insights into the efficacy of various channel modeling techniques for optimizing 5G industrial network planning.

Liang Yuan

Coal is a guarantee and ballast of China’s energy security, and the Chinese government strongly supports the development of the coal industry. The level of coal mine gas control and utilization in China has increased to a new level, which has effectively promoted the high-quality development of China’s coal industry. In this new situation, the future innovative development of China’s coal mine gas control is discussed in six directions: continuously promoting the construction of transparent mines, paying attention to the atypical coal and gas outburst disasters, disaster monitoring and early warning, precise development of coal mine gas, industrialization technology of low-concentration gas, and coal mine gas control under the dual-carbon target. This will continue to contribute to the high-quality development of China’s coal industry and promote green, low-carbon transformations, and high-quality development in the coal industry.

Danny Weyns, Thomas Vogel

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community.

Markus Dahlmanns, Johannes Lohmöller, Jan Pennekamp et al.

The ongoing trend to move industrial appliances from previously isolated networks to the Internet requires fundamental changes in security to uphold secure and safe operation. Consequently, to ensure end-to-end secure communication and authentication, (i) traditional industrial protocols, e.g., Modbus, are retrofitted with TLS support, and (ii) modern protocols, e.g., MQTT, are directly designed to use TLS. To understand whether these changes indeed lead to secure Industrial Internet of Things deployments, i.e., using TLS-based protocols, which are configured according to security best practices, we perform an Internet-wide security assessment of ten industrial protocols covering the complete IPv4 address space. Our results show that both, retrofitted existing protocols and newly developed secure alternatives, are barely noticeable in the wild. While we find that new protocols have a higher TLS adoption rate than traditional protocols (7.2% vs. 0.4%), the overall adoption of TLS is comparably low (6.5% of hosts). Thus, most industrial deployments (934,736 hosts) are insecurely connected to the Internet. Furthermore, we identify that 42% of hosts with TLS support (26,665 hosts) show security deficits, e.g., missing access control. Finally, we show that support in configuring systems securely, e.g., via configuration templates, is promising to strengthen security.

Ali Ahmad Malik

Assembly is a key industrial process to achieve finished goods. Driven by market demographics and technological advancements, industrial assembly has evolved through several phases i.e. craftmanship, bench assembly, assembly lines and flexible assembly cells. Due to the complexity and variety of assembly tasks, besides significant advancement of automation technologies in other manufacturing activities, humans are still considered vital for assembly operations. The rationalization of manufacturing automation has considerably remained away from assembly systems. The advancement in assembly has only been in terms of better scheduling of work tasks and avoiding of wastes. With smart manufacturing technologies such as collaborative robots, additive manufacturing, and digital twins, the opportunities have arisen for the next reshaping of assembly systems. The new paradigm promises a higher degree of automation yet remaining flexible. This may result into a new manufacturing paradigm driven by the advancement of new technologies, new customer expectations and by establishing new kinds of manufacturing systems. This study explores the future collaborative assembly cells, presents a generic framework to develop them and the basic building blocks.

Jiawen Kang, Dongdong Ye, Jiangtian Nie et al.

The emerging industrial metaverses realize the mapping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and operation lines by Industrial Internet of Things (IIoT), and thus conduct effective data analysis and decision-making, thereby enhancing the production efficiency of the physical space, reducing operating costs, and maximizing commercial value. However, there still exist bottlenecks when integrating metaverses into IIoT, such as the privacy leakage of sensitive data with commercial secrets, IIoT sensing data freshness, and incentives for sharing these data. In this paper, we design a user-defined privacy-preserving framework with decentralized federated learning for the industrial metaverses. To further improve privacy protection of industrial metaverse, a cross-chain empowered federated learning framework is further utilized to perform decentralized, secure, and privacy-preserving data training on both physical and virtual spaces through a hierarchical blockchain architecture with a main chain and multiple subchains. Moreover, we introduce the age of information as the data freshness metric and thus design an age-based contract model to motivate data sensing among IIoT nodes. Numerical results indicate the efficiency of the proposed framework and incentive mechanism in the industrial metaverses.

Damien Burlet-Vienney, Bertrand Galy, Kariane Cusson Bertrand et al.

Vehicles falling off two-post above-ground (2PAG) lifts is a fairly frequent occurrence. As only limited knowledge is available about the determinants influencing the stability of lifting vehicles with a 2PAG lift, two experimental designs were carried out in order to have quantitative data. This paper addresses support pad slippage as a result of external forces being exerted on a vehicle. The experimental design is based on the consultation of the key players that identify the main issues related to the support pads. The controlled factors chosen in this experimental design were lift support pad type and position, smear on pads, arm locking and external force type. Based on the analysis of variance, factors that had a significant influence on the support pad slippage were (i) support pad type, (ii) external force type and (iii) the interaction between those two controlled factors. Arm locking and support pad position were not statistically significant. From a practical standpoint, initial placement of the support pad is, however, a major safety measure, as support pad slippage went up to 53% of the pad half-width. These results should challenge 2PAG lift manufacturers and vehicle manufacturers to come up with support pad and lifting point designs, respectively, that would reduce this inherent risk of the 2PAG lifts.

Julius Keller, Flavio Antonio Coimbra Mendonca, Daniel Kwasi Adjekum

An increase in evidence-based studies into the deleterious effects of fatigue on flight operations has been reported by key aviation groups globally. The collegiate aviation flight training environment has not been researched at the same level when compared to military and airline operations. College aged students are unique in the sense that they are tasked with classwork, studying, participation in student organizations, social activities, and often have part time jobs within and outside of the academic environment. These conditions may cause errors, incidents, accidents, poor academic performance, and undesirable health metrics. The purpose of this study was to understand fatigue as a multi-factorial dimension and to assess potential relationships among these factors using hypothesized measurement models. The research team distributed the Collegiate Aviation Fatigue Inventory II (CAFI-II) to eight small, medium, and large collegiate aviation programs in the United States. The CAFI-II primarily focuses on fatigue awareness, causes and symptoms of fatigue, and lifestyle choices. Four hundred and twenty-two (<i>n</i> = 422) valid responses were obtained. Results suggested a direct predictive relationship between fatigue in collegiate flight training and the perceptions of respondents of conditions that are known to cause fatigue. Findings also suggested that respondents who had a favorable perception of fatigue risk and management programs had a better understanding of the causes of fatigue.

Amanda Wechsler, Ana Cristina Zordan Rani-Yonamine

The COVID-19 pandemic can produce considerable impacts on the mental health of healthcare professionals, as they face several stressors at work. Purpose: This study aims to assess Brazilian healthcare professionals’ mental health and quality of life. Method: participants were divided into four groups according to their occupational fields: administrative(n=129), emergency (n=39), primary care (n=257) and specialties (n=51). Participants completed a sociodemographic questionnaire, the World Health Organization Quality of Life-BREF and the Self-Reporting Questionnaire. Data analysis: Data were analyzed with Variance Analysis, Chi-square, and linear and logistic regressions. Results: Results showed that health providers have an average quality of life but presented high rates of mental disorders, especially those working in emergency and primary care. Some risk factors, such as demographic (gender, age, educational attainment, marital status), lifestyle (physical activity, diet, religion, social support, leisure), and labor (job satisfaction, job congruence, presence of deaths at work) were observed. Conclusions: This research shows the importance of designing specific psychological interventions for this population, which seems to be at risk for more severe mental conditions.

Tiago Espinha Gasiba, Ulrike Lechner, Maria Pinto-Albuquerque

Awareness of cybersecurity topics facilitates software developers to produce secure code. This awareness is especially important in industrial environments for the products and services in critical infrastructures. In this work, we address how to raise awareness of software developers on the topic of secure coding. We propose the "CyberSecurity Challenges", a serious game designed to be used in an industrial environment and address software developers' needs. Our work distils the experience gained in conducting these CyberSecurity Challenges in an industrial setting. The main contributions are the design of the CyberSecurity Challenges events, the analysis of the perceived benefits, and practical advice for practitioners who wish to design or refine these games.