Hasil untuk "Industrial electrochemistry"

Shen Yin, S. Ding, Xiaochen Xie et al.

S. Kouro, M. Malinowski, K. Gopakumar et al.

Multilevel converters have been under research and development for more than three decades and have found successful industrial application. However, this is still a technology under development, and many new contributions and new commercial topologies have been reported in the last few years. The aim of this paper is to group and review these recent contributions, in order to establish the current state of the art and trends of the technology, to provide readers with a comprehensive and insightful review of where multilevel converter technology stands and is heading. This paper first presents a brief overview of well-established multilevel converters strongly oriented to their current state in industrial applications to then center the discussion on the new converters that have made their way into the industry. In addition, new promising topologies are discussed. Recent advances made in modulation and control of multilevel converters are also addressed. A great part of this paper is devoted to show nontraditional applications powered by multilevel converters and how multilevel converters are becoming an enabling technology in many industrial sectors. Finally, some future trends and challenges in the further development of this technology are discussed to motivate future contributions that address open problems and explore new possibilities.

Da Chen, Longhua Tang, Jinghong Li

C. Cancino, J. Merigó, Freddy Coronado et al.

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

Jian Yang, Wei Zhang, Jiajun Wu et al.

Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and timing semantics. In this work, we propose InCoder-32B-Thinking, trained on the data from the Error-driven Chain-of-Thought (ECoT) synthesis framework with an industrial code world model (ICWM) to generate reasoning traces. Specifically, ECoT generates reasoning chains by synthesizing the thinking content from multi-turn dialogue with environmental error feedback, explicitly modeling the error-correction process. ICWM is trained on domain-specific execution traces from Verilog simulation, GPU profiling, etc., learns the causal dynamics of how code affects hardware behavior, and enables self-verification by predicting execution outcomes before actual compilation. All synthesized reasoning traces are validated through domain toolchains, creating training data matching the natural reasoning depth distribution of industrial tasks. Evaluation on 14 general (81.3% on LiveCodeBench v5) and 9 industrial benchmarks (84.0% in CAD-Coder and 38.0% on KernelBench) shows InCoder-32B-Thinking achieves top-tier open-source results across all domains.GPU Optimization

Chao Yin, Hongzhe Yue, Qing Han et al.

Automated semantic understanding of dense point clouds is a prerequisite for Scan-to-BIM pipelines, digital twin construction, and as-built verification--core tasks in the digital transformation of the construction industry. Yet for industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: TLS acquisitions of water treatment plants, chiller halls, and pumping stations exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks (e.g., S3DIS or ScanNet) cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset comprising 612 million expertly labelled points at 6 mm resolution from 13 water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest and most demanding testbed for industrial 3D scene understanding to date. We further establish the first industrial cross-paradigm benchmark, evaluating nine representative methods across fully supervised, weakly supervised, unsupervised, and foundation model settings under a unified benchmark protocol. The best supervised method achieves 55.74% mIoU, whereas zero-shot Point-SAM reaches only 15.79%--a 39.95 percentage-point gap that quantifies the unresolved domain-transfer challenge for industrial TLS data. Systematic analysis reveals that this gap originates from a dual crisis: statistical rarity (215:1 imbalance, 3.5x more severe than S3DIS) and geometric ambiguity (tail-class points share cylindrical primitives with head-class pipes) that frequency-based re-weighting alone cannot resolve. Industrial3D, along with benchmark code and pre-trained models, will be publicly available at https://github.com/pointcloudyc/Industrial3D.

Shahid Mumtaz, Ahmed Alsohaily, Zhibo Pang et al.

Francesc Sabater, Ouns El Harzli, Geert-Jan Besjes et al.

Optimization via decoded quantum interferometry (DQI) has recently gained a great deal of attention as a promising avenue for solving optimization problems using quantum computers. In this paper, we apply DQI to an industrial optimization problem in the automotive industry: the vehicle option-package pricing problem. Our main contributions are 1) formulating the industrial problem as an integer linear program (ILP), 2) converting the ILP into instances of max-XORSAT, and 3) developing a detailed quantum circuit implementation for belief propagation, a heuristic algorithm for decoding LDPC codes. Thus, we provide a full implementation of the DQI algorithm using Belief Propagation, which can be applied to any industrially relevant ILP by first transforming it into a max-XORSAT instance. We also evaluate the effectiveness of our implementation by benchmarking it against both Gurobi and a random sampling baseline.

Leila Ismail, Abdelmoneim Abdelmoti, Arkaprabha Basu et al.

With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the Internet of Things, Artificial Intelligence, machine learning, and real-time big data analytics, there is a unique opportunity for efficient predictive maintenance to forecast equipment failures for real-time intervention and optimize maintenance actions, as traditional reactive and preventive maintenance practices are often inadequate to meet the requirements for the industry to provide quality-of-services of operations. Central to this evolution is digital twin technology, an adaptive virtual replica that continuously monitors and integrates sensor data to simulate and improve asset performance. Despite remarkable progress in digital twin implementations, such as considering DT in predictive maintenance for industrial engineering. This paper aims to address this void. We perform a retrospective analysis of the temporal evolution of the digital twin in predictive maintenance for industrial engineering to capture the applications, middleware, and technological requirements that led to the development of the digital twin from its inception to the AI-enabled digital twin and its self-learning models. We provide a layered architecture of the digital twin technology, as well as a taxonomy of the technology-enabled industrial engineering applications systems, middleware, and the used Artificial Intelligence algorithms. We provide insights into these systems for the realization of a trustworthy and efficient smart digital-twin industrial engineering ecosystem. We discuss future research directions in digital twin for predictive maintenance in industrial engineering.

Haijun Li, Tianqi Shi, Zifu Shang et al.

Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in large language models (LLMs) significantly enhancing translation quality. However, applying general-purpose MT models to industrial scenarios reveals critical limitations due to domain-specific terminology, cultural nuances, and stylistic conventions absent in generic benchmarks. Existing evaluation frameworks inadequately assess performance in specialized contexts, creating a gap between academic benchmarks and real-world efficacy. To address this, we propose a three-level translation capability framework: (1) Basic Linguistic Competence, (2) Domain-Specific Proficiency, and (3) Cultural Adaptation, emphasizing the need for holistic evaluation across these dimensions. We introduce TransBench, a benchmark tailored for industrial MT, initially targeting international e-commerce with 17,000 professionally translated sentences spanning 4 main scenarios and 33 language pairs. TransBench integrates traditional metrics (BLEU, TER) with Marco-MOS, a domain-specific evaluation model, and provides guidelines for reproducible benchmark construction. Our contributions include: (1) a structured framework for industrial MT evaluation, (2) the first publicly available benchmark for e-commerce translation, (3) novel metrics probing multi-level translation quality, and (4) open-sourced evaluation tools. This work bridges the evaluation gap, enabling researchers and practitioners to systematically assess and enhance MT systems for industry-specific needs.

Qishan Wang, Shuyong Gao, Junjie Hu et al.

Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. However, the real-world effectiveness of MUAD methods is questioned due to limitations in current Industrial Anomaly Detection (IAD) datasets. These datasets contain numerous classes that are unlikely to be produced by the same factory and fail to cover multiple structures or appearances. Additionally, the defects do not reflect real-world characteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial Anomaly Detection (HSS-IAD) dataset, which contains 8,580 images of metallic-like industrial parts and precise anomaly annotations. These parts exhibit variations in structure and appearance, with subtle defects that closely resemble the base materials. We also provide foreground images for synthetic anomaly generation. Finally, we evaluate popular IAD methods on this dataset under multi-class and class-separated settings, demonstrating its potential to bridge the gap between existing datasets and real factory conditions. The dataset is available at https://github.com/Qiqigeww/HSS-IAD-Dataset.

Anju Rani, Daniel Ortiz-Arroyo, Petar Durdevic

In recent years, 3D point clouds (PCs) have gained significant attention due to their diverse applications across various fields, such as computer vision (CV), condition monitoring (CM), virtual reality, robotics, autonomous driving, etc. Deep learning (DL) has proven effective in leveraging 3D PCs to address various challenges encountered in 2D vision. However, applying deep neural networks (DNNs) to process 3D PCs presents unique challenges. This paper provides an in-depth review of recent advancements in DL-based industrial CM using 3D PCs, with a specific focus on defect shape classification and segmentation within industrial applications. Recognizing the crucial role of these aspects in industrial maintenance, the paper offers insightful observations on the strengths and limitations of the reviewed DL-based PC processing methods. This knowledge synthesis aims to contribute to understanding and enhancing CM processes, particularly within the framework of remaining useful life (RUL), in industrial systems.

Abdul Ahad, Mohammad Tahir

Due to the rise of connected devices, a decentralised, patient-centred paradigm is being adopted in healthcare as an alternative to the traditional hospital and specialist-focused approach. As the healthcare sector expands, more applications will be connected to the network, producing data of various shapes and sizes that will allow for customised and remote healthcare services. Future intelligent healthcare will include a combination of 6G and the Internet of Things (IoT) that will address current limitations related to cellular coverage, network performance and security issues. This paper discusses and sheds light on prospects, associated challenges and future directions.

Ashwani Kumar, S.K. Tripathi, Mohd. Shkir et al.

The next-generation solar cells are metal halide-based hybrid perovskite solar cells (PSCs), and we have already passed the point where three generations of solar cells were developed. PSCs is one of the younger class of photovoltaic devices among others and have provided a steep change in photovoltaic research ever the date which has experienced an extraordinary increase in efficiency along with its stability and have appeared as an extremely efficient photovoltaic technology. The biggest problems with this material are keeping it from degrading in wet environments and keeping it from overheating. Besides this, the second concerned issue is the toxicity of lead (Pb), which could be an encumbrance for large-scale production due to environmental concerns. Consequently, significant effort has been devoted to finding alternative halide perovskites with excellent optoelectronics applications, consisting of completely inorganic components of low-cost, less toxic elements. The use of environmentally friendly materials in all-inorganic lead-free perovskites aims to alleviate these problems. Aside from that, there is a diverse spectrum of inorganic materials that can form perovskite structures, providing material flexibility for specific applications. This review paper provides a comprehensive overview of the literature on Pb-free perovskite materials, discussing their design methods, morphologies, and environmental stability problems. First, a comparison is made between the elements of Tin (Sn) and Pb, and some Pb is replaced with Sn. The fact that Germanium (Ge), transition metals, and elements from other columns of the periodic table can replace this element is emphasized. We conclude our study with a critical assessment of the current issues and potential future directions for this rapidly developing discipline.

Wenjiao Zhao, Julian Gebauer, Thomas Bergfeldt et al.

Li_1.11(Ni_0.4Mn_0.4Co_0.2)O₂ powders were chemically delithiated by (NH₄)₂S₂O₈ oxidizer to obtain Li_x(Ni_0.4Mn_0.4Co_0.2)O₂ powders. The thermal behavior of two delithiated specimens, Li_0.76Ni_0.41Mn_0.42Co_0.17O_2.10 and Li_0.48Ni_0.38Mn_0.46Co_0.16O_2.07, was studied compared to the pristine specimen. Phase transitions at elevated temperatures were investigated by simultaneous thermal analysis (STA) and the gas evolution accompanying the phase transitions was analyzed by mass spectroscopy and an oxygen detector. The enthalpy of two delithiated samples and a pristine specimen were measured by a high temperature drop solution calorimeter. Based on these results, the enthalpies of formation were calculated.

Sung-Beom Kim, So-Yeon Ahn, Ji-Hwan Kim et al.

Recently, Ni-rich ternary transition metal oxides (Li(NiCoMn)O2, NCM) containing more than 80% Ni have been extensively studied as high-capacity cathodes. Herein, we synthesized a quaternary cathode, Ni-rich Al-doped NCM, using a facile solvothermal method without an additional Al doping process. Compared to an undoped NCM cathode, the quaternary Ni-rich cathode exhibited improved lithium-ion battery performance (retentions of 82.0% and 65.8% after 100 and 250 cycles, respectively). In particular, the superior stability of the quaternary Ni-rich cathode may result from the stable cathode structure resulting from Al doping.

Mitsuru Yamada, Dr. Mika Fukunishi, Nobuo Ando et al.

Abstract This study was conducted to improve the rate capability and cyclability of cathodes for lithium‐ion batteries (LIBs) with a hybrid cathode structure. The through‐holed LIB cathode material and activated carbon layers formed on each side of a current collector were drilled with a picosecond pulsed laser beam to prepare the cathode structure. The hybrid cathodes exhibited excellent rate capabilities of 93 % capacity retention at 100 C. The results were dependent on the weight percentage of the activated carbon relative to the total weight of the active materials and on the difference in discharge/charge voltages between the LIB cathode and activated carbon materials. The cathode had cycle stability at 50 C during 100 cycles. The performance characteristics of the hybrid cathode, the through‐holed and nontreated LIB cathodes, and the nontreated activated carbon cathodes were compared. In the Ragone plot, the hybrid cathode was located in the region where conventional through‐holed and nontreated cathodes would not be located.

Dmitry Zhevnenko, Mikhail Kazantsev, Ilya Makarov

The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods rely on one approach to feature extraction in which the prediction occurs. We proposed a technique to build a scalable model that combines multiple different feature extractor blocks. A new model based on sequential sensor space analysis achieves state-of-the-art results on the C-MAPSS benchmark for equipment remaining useful life estimation. The resulting model performance was validated including the prediction changes with scaling.

Sérgio F. Chevtchenko, Elisson da Silva Rocha, Monalisa Cristina Moura Dos Santos et al.

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities.