Hasil untuk "Industrial directories"

Christian A. Cancino, J. Merigó, Freddy Coronado et al.

A. Colombo, S. Karnouskos, O. Kaynak et al.

Jian Yang, Wei Zhang, Jiajun Wu et al.

Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.

Xu Haotian, Guo Bingjing, Han Jianhai et al.

Stroke patients with hemiplegia require personalized upper-limb rehabilitation, yet designing safe and effective robot-assisted trajectories that mimic natural human movement remains a significant challenge. This paper proposes a trajectory planning and optimization method to address this need by leveraging multi-objective constrained reinforcement learning. The method involves dynamically capturing motion data from the patient's healthy limb to define personalized Activities of Daily Living (ADL). A reinforcement learning algorithm, guided by a specially designed reward-punishment function, then optimizes the trajectory with objectives for smoothness, jerk minimization, and accurate tracking of key points. The approach was validated on a 4-degree-of-freedom (4-DOF) upper limb rehabilitation robot, which successfully achieved multi-joint coordinated trajectory tracking based on the learned ADL movements. The experiments confirm the method's effectiveness in designing personalized rehabilitation trajectories that improve the continuity and smoothness of robot-assisted movements, offering a promising solution for patient-specific therapy.

Keke Gai, Yulu Wu, Liehuang Zhu et al.

Contemporarily, two emerging techniques, blockchain and edge computing, are driving a dramatical rapid growth in the field of Internet-of-Things (IoT). Benefits of applying edge computing is an adoptable complementarity for cloud computing; blockchain is an alternative for constructing transparent secure environment for data storage/governance. Instead of using these two techniques independently, in this article, we propose a novel approach that integrates IoT with edge computing and blockchain, which is called blockchain-based Internet of Edge model. The proposed model, designed for a scalable and controllable IoT system, sufficiently exploits advantages of edge computing and blockchain to establish a privacy-preserving mechanism while considering other constraints, such as energy cost. We implement experiment evaluations running on Ethereum. According to our data collections, the proposed model improves privacy protections without lowering down the performance in an energy-efficient manner.

Jun-Song Fu, Yun Liu, H. Chao et al.

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

Yingfeng Zhang, Zhengang Guo, Jingxiang Lv et al.

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

Despina Tomkou, George Fatouros, Andreas Andreou et al.

This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.

Ruiyang Ma, Tianhao Wei, Jiaxi Zhang et al.

As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.

Naval Kishore Mehta, Arvind, Himanshu Kumar et al.

Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.

Ding Wang, Wenting Li, Ping Wang

Dozens of two-factor authentication schemes have been proposed to secure real-time data access in industrial wireless sensor networks (WSNs). However, more often than not, the protocol designers advocate the merits of their scheme, but do not reveal (or unconsciously ignoring) the facets on which their scheme performs poorly. Such lack of an objective, comprehensive measurement leads to the unsatisfactory “break-fix-break-fix” cycle in this research area. In this paper, we make an attempt toward breaking this undesirable cycle by proposing a systematical evaluation framework for schemes to be assessed objectively, revisiting two foremost schemes proposed by Wu et al. (2017) and Srinivas et al. (2017) to reveal the challenges and difficulties in designing a sound scheme, and conducting a measurement of 44 representative schemes under our evaluation framework, thereby providing the missing evaluation for two-factor schemes in industrial WSNs. This work would help increase awareness of current measurement issues and improve the scientific process in our field.

F. Al-turjman, Sinem Alturjman

Industrial Internet of Things (IIoTs) is the fast growing network of interconnected things that collects and exchange data using embedded sensors planted everywhere. Several IIoT applications such as the ones related to healthcare systems are expected to widely utilize the evolving 5G technology. This 5G-inspired IIoT paradigm in healthcare applications enables the users to interact with various types of sensors via secure wireless medical sensor networks (WMSNs). Users of 5G networks should interact with each other in a seamless secure manner. And thus, security richness is highly coveted for the real time wireless sensor network systems. Asking users to verify themselves before every interaction is a tedious, time-consuming process that disrupts inhabitants’ activities, and degrades the overall healthcare system performance. To avoid such problems, we propose a context-sensitive seamless identity provisioning (CSIP) framework for the IIoT. CSIP proposes a secure mutual authentication approach using hash and global assertion value to prove that the proposed mechanism can achieve the major security goals of the WMSN in a short time period.

J. Li, L. Lyu, X. Liu et al.

Due to resource constraints and working surroundings, many IIoT nodes are easily hacked and turn into zombies from which to launch attacks. It is challenging to detect such networked zombies rooted behind the Internet for any individual defender. In this article, we combine federated learning (FL) and fog/edge computing to combat malicious codes. Our protocol trains a global optimized model based on distributed datasets of collaborators while removing the data and communication constraints. The FL-based detection protocol maximizes the values of distributed data samples, resulting in an accurate model timely. On top of the protocol, we place mitigation intelligence in a distributed and collaborative manner. Our approach improves accuracy, eliminates mitigation time, and enlarges attackers’ expense within a defense alliance. Comprehensive evaluations confirm that the cost incurred is 2.7 times larger, the mitigation response time is 72% lower, and the accuracy is 47% higher on average. Besides, the protocol evaluation shows the detection accuracy is approximately 98% in the FL, which is almost the same as centralized training.

Linda Kölbel, Markus Hornsteiner, Stefan Schönig

In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.

Xi Jiang, Jian Li, Hanqiu Deng et al.

In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.

Siqi Wang, Chao Liang, Yunfan Gao et al.

Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://github.com/Tongji-KGLLM/IndustryScope.

Francesco Sanfedino, Paolo Iannelli, Daniel Alazard et al.

To overcome the innovation gap of the Guidance, Navigation and Control (GNC) design process between research and industrial practice a benchmark of industrial relevance has been developed and is presented. This initiative is driven as well by the necessity to train future GNC engineers and the GNC space community on a set of identified complex problems. It allows to demonstrate the relevance of state-of-the-art modeling, control and analysis algorithms for future industrial adoption. The modeling philosophy for robust control synthesis, analysis including the control architecture that enables the simulation of the mission, i.e. the acquisition of a high pointing space mission, are provided.

Mariia Sulimovska

У статті сформульовано актуальне завдання оцінювання захищеності інформації з обмеженим доступом, яка циркулює в підсистемі органів військового управління системи управління оперативного угруповання військ (сил) під час виконання операцій у зоні проведення бойових дій та запропоновано її вирішення. Аналіз функціонування оперативного угруповання військ (сил) підтверджує, що ця проблема є актуальною і потребує вирішення в умовах широкомасштабної збройної агресії російської федерації проти України. Метою статті є вдосконалення методики оцінювання захищеності інформації з обмеженим доступом, яка циркулює в підсистемі органів військового управління, що здійснене для запобігання витоку інформації та недопущення втрати її матеріальних носіїв й унеможливлення використання цих відомостей противником. Методи дослідження: системний аналіз, метод моделювання, експертні оцінки, метод порівняльного аналізу, метод SWOT-аналізу, метод статистичного аналізу, метод сценарного аналізу. Зазначений методологічний підхід дав змогу належно оцінити захищеність інформації з обмеженим доступом. Аналіз останніх досліджень і публікацій, присвячених оцінюванню захищеності інформації свідчить про необхідність актуалізації та вдосконалення такого оцінювання з урахуванням загроз захищеності, зумовлених високою динамікою бойових дій. У статті показано, що вирішення цієї проблеми потребує системного підходу та ефективного застосування показників оцінювання захищеності інформації з обмеженим доступом в підсистемі органів військового управління. Запропоновано визначення коефіцієнту захищеності інформації з обмеженим доступом, який характеризує ступінь ефективності функціонування системи забезпечення захисту інформації в підсистемі органів військового управління під час виконання операцій. Цей показник обмежується статтями Зводу відомостей, що становить державну таємницю та пунктами Переліку службової інформації, що належать до структур сектору оборони України (Збройних Сил України та Міністерства оборони України). Удосконалена методика оцінювання захищеності інформації з обмеженим доступом складається з восьми блоків, що охоплюють формування вихідних даних, розрахунок коефіцієнтів захищеності та оцінку потенційної шкоди від витоку інформації або втрати відомостей. Методика також передбачає виявлення причин невідповідності вимогам, розробку рекомендацій для підвищення ефективності забезпечення захисту та визначення загального рівня безпеки інформації. Її реалізація дає змогу забезпечити відповідність системи забезпечення захисту інформації з обмеженим доступом в підсистемі органів військового управління встановленим вимогам та мінімізувати ризики втрати матеріальних носіїв інформації. Науковою новизною є впровадження чотирьохступеневої системи обмеження доступу до інформації з грифами секретності «Особливо важливо», «Цілком таємно», «Таємно» та грифом обмеження доступу «Для службового користування». Ця система передбачена в проєкті Закону України «Про безпеку класифікованої інформації» з урахуванням положень Стратегії національної безпеки України, стандартів безпеки НАТО та Європейського Союзу. Її впровадження дає змогу створити нову методику оцінювання захищеності інформації з обмеженим доступом та вдосконалити нормативно-правову базу. Удосконалена методика сприяє розвитку теорії безпеки інформації, впроваджуючи міжнародні стандарти НАТО та Європейського Союзу в контексті національної оборони України. Це дає змогу створити уніфіковану систему оцінювання захищеності інформації та адаптувати підходи до оцінювання захищеності інформації з урахуванням сучасних загроз і специфіки оперативного середовища. Практична значущість методики надає можливість своєчасно оцінювати рівень захищеності інформації, виявляти причини виникнення загроз і розробляти заходи для мінімізації ризиків витоку або втрати відомостей. Її впровадження підвищує ефективність управління оперативним угруповання військ (сил) в зоні проведення бойових дій. Напрямом подальших досліджень є удосконалення моделі оцінювання шкоди оперативному угрупованню військ (сил) у разі витоку інформації з обмеженим доступом, яка циркулює в підсистемі органів військового управління, а також розробка та впровадження відповідних положень у нормативно-правові акти, що регламентують організацію та забезпечення безпеки інформації.

Mimi Ma, De-biao He, Neeraj Kumar et al.