Hasil untuk "Engineering machinery, tools, and implements"

Xiao Qin, Xingyi Song, Tong Liu et al.

We present LoRM (Language of Rotating Machinery), a self-supervised framework for multi-modal rotating-machinery signal understanding and real-time condition monitoring. LoRM is built on the idea that rotating-machinery signals can be viewed as a machine language: local signals can be tokenised into discrete symbolic units, and their future evolution can be predicted from observed multi-sensor context. Unlike conventional signal-processing methods that rely on hand-crafted transforms and features, LoRM reformulates multi-modal sensor data as a token-based sequence-prediction problem. For each data window, the observed context segment is retained in continuous form, while the future target segment of each sensing channel is quantised into a discrete token. Then, efficient knowledge transfer is achieved by partially fine-tuning a general-purpose pre-trained language model on industrial signals, avoiding the need to train a large model from scratch. Finally, condition monitoring is performed by tracking token-prediction errors as a health indicator, where increasing errors indicate degradation. In-situ tool condition monitoring (TCM) experiments demonstrate stable real-time tracking and strong cross-tool generalisation, showing that LoRM provides a practical bridge between language modelling and industrial signal analysis. The source code is publicly available at https://github.com/Q159753258/LormPHM.

Marie Bolzer, Sébastien Duval, Marine Minier

The problem of finding a minimal circuit to implement a given function is one of the oldest in electronics. It is known to be NP-hard. Still, many tools exist to find sub-optimal circuits to implement a function. In electronics, such tools are known as synthesisers. However, these synthesisers aim to implement very large functions (a whole electronic chip). In cryptography, the focus is on small functions, hence the necessity for new dedicated tools for small functions. Several tools exist to implement small functions. They differ by their algorithmic approach (some are based on Depth-First-Search as introduced by Ullrich in 2011, some are based on SAT-solvers like the tool desgined by Stoffelen in 2016, some non-generic tools use subfield decomposition) and by their optimisation criteria (some optimise for circuit size, others for circuit depth, and some for side-channel-protected implementations). However, these tools are limited to functions operating on less than 5 bits, sometimes 6 bits for quadratic functions, or to very simple functions. The limitation lies in a high computing time. We propose a new tool (The tool is provided alongside the IEEE article with CodeOcean and at https://github.com/seduval/implem-quad-sbox) to implement quadratic functions up to 9 bits within AND-depth 1, minimising the number of AND gates. This tool is more time-efficient than previous ones, allowing to explore larger implementations than others on 6 bits or less and allows to reach larger sizes, up to 9 bits.

Anastasiia Shuba, Ruslan Umarkhanov, Ekaterina Bogdanova et al.

The importance of assessing the microbiological safety of food products is beyond doubt, which is also true for milk and dairy products. The goal of this work was to evaluate the changes in the composition of the gas phase in milk based on signals from chemical sensors to predict the quantity of the bacteria in the milk samples. The gas phase in raw milk samples and samples during pasteurization, as well as for a standard (a model aqua solution of macronutrients and minerals), was studied using an array of sensors with polycomposite coatings, including those contaminated with <i>E. coli</i> bacteria. Assessment of microbiological indicators was carried out according to GOST in parallel with the gas-phase analysis. The applicability of the results obtained on model systems was assessed using milk samples, including those containing other types of pathogenic microorganisms <i>(Staphylococcus aureus</i>, <i>Klebsiella</i> spp., etc.). It was found that the obtained models can be used to assess the presence and quantity of <i>E. coli</i> in milk at the pasteurization stage.

Serap Yigit Gezgin, Yasemin Gundogdu Kabakci, Hamdi Sukur Kilic

CZTS (C-I/C-II) ultrathin films in 61 nm and 313 nm thicknesses were grown on microscopic glass and n-Si wafer substrates via laser ablation, respectively. C-II ultrathin film with higher thickness has a more developed crystal structure and consists of larger particles compared to C-I ultrathin film with reduced thickness. C-II ultrathin film absorbs more photons and has a lower band gap. The photovoltaic performance of the produced Ag/CZTS (C-II)/n-Si/Al solar cell is higher compared to the other solar cell-based C-I ultrathin film. The more improved crystal structure of C-II ultrathin film has increased the efficiency of the solar cell. The calculated photovoltaic parameters of the solar cells modeled with the SCAPS-1D simulation program were found to be compatible with the experimental parameters. This situation has proven that the operating performance of solar cells is reliable.

Hana Švehláková, Petr Plohák, Barbara Stalmachová et al.

The D1 Paskov spoil heap is a smaller brownfield covering an area of 71,188 m², located in the former Paskov mining region. It serves as a model area for reclamation planning, based on a comprehensive assessment of its natural conditions and the risks posed by contamination from hazardous elements and erosion processes. Data for this assessment was collected through field research conducted between 2023 and 2025. In September 2023, additional fieldwork and mapping were carried out using unmanned aerial vehicles equipped with two types of sensors: an RGB camera and LiDAR. The dump is primarily covered with ruderal vegetation, with the summit plateau dominated by the expansive grass species <i>Calamagrostis epigejos</i>. With appropriate management, the plant communities on the western and northern slopes have the potential to develop into conservation-significant habitats. However, the southwestern slope presents challenges due to active rill erosion and contamination. Stabilization measures are required to prevent further degradation in this area.

Muhammad Tayyab Khan, Zane Yong, Lequn Chen et al.

Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpreting complex multi-view drawings with dense annotations remains challenging using manual methods, generic optical character recognition (OCR) systems, or traditional deep learning approaches, due to varied layouts, orientations, and mixed symbolic-textual content. To address these challenges, this paper proposes a three-stage hybrid framework for the automated interpretation of 2D multi-view engineering drawings using modern detection and vision language models (VLMs). In the first stage, YOLOv11-det performs layout segmentation to localize key regions such as views, title blocks, and notes. The second stage uses YOLOv11-obb for orientation-aware, fine-grained detection of annotations, including measures, GD&T symbols, and surface roughness indicators. The third stage employs two Donut-based, OCR-free VLMs for semantic content parsing: the Alphabetical VLM extracts textual and categorical information from title blocks and notes, while the Numerical VLM interprets quantitative data such as measures, GD&T frames, and surface roughness. Two specialized datasets were developed to ensure robustness and generalization: 1,000 drawings for layout detection and 1,406 for annotation-level training. The Alphabetical VLM achieved an overall F1 score of 0.672, while the Numerical VLM reached 0.963, demonstrating strong performance in textual and quantitative interpretation, respectively. The unified JSON output enables seamless integration with CAD and manufacturing databases, providing a scalable solution for intelligent engineering drawing analysis.

Bertrand Meyer

Vibe coding, the much-touted use of AI techniques for programming, faces two overwhelming obstacles: the difficulty of specifying goals ("prompt engineering" is a form of requirements engineering, one of the toughest disciplines of software engineering); and the hallucination phenomenon. Programs are only useful if they are correct or very close to correct. The solution? Combine the creativity of artificial intelligence with the rigor of formal specification methods and the power of formal program verification, supported by modern proof tools.

Johan Cederbladh, Loek Cleophas, Eduard Kamburjan et al.

With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.

Smita Bharne, Pawan Bhaladhare

Generating captions automatically for images has been a challenging task, requiring the integration of image processing and natural language processing techniques. In this study, we propose a system that focuses on generating captions for online social network users’ profile images using a Bootstrapping Language–Image Pre-Training Model. Our approach leverages pre-training techniques, enabling the model to learn visual and textual representations from large datasets, which are then fine-tuned on a task-specific dataset. By utilizing this methodology, our proposed system demonstrates promising performance in generating captions for online social network users’ profile images. The model effectively combines visual and textual information to generate informative and contextually relevant captions. This can greatly enhance user engagement and personalization on social media platforms, as users’ profile images are accompanied by meaningful captions that accurately describe the content and context of the images. The proposed system shows its performance on the task of caption generation for online social network users’ profile images. Furthermore, we show that our model can be used to identify scam (fake) profiles on online social networks by generating more accurate and informative captions for real profiles than for fake ones. By leveraging the power of pre-training and bootstrapping techniques, our model showcases its potential in enhancing user experiences, improving platform security, and promoting a more trustworthy online social environment. The proposed system has the potential to improve the authenticity and trustworthiness of user profiles on online social networks.

Everton Cavalcante, Thais Batista, Flavio Oquendo

Modern systems are increasingly connected and more integrated with other existing systems, giving rise to \textit{systems-of-systems} (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within Software Engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The \textit{International Workshop on Software Engineering for Systems-of-Systems} (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in Software Engineering for SoS.

Xavier Amatriain

Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we provide a survey of tools for prompt engineers.

Toru YAMAZAKI, Ryo IWAMOTO, Kai KURIHARA et al.

This study focuses on the energy transmissibility, which is called coupling loss factor (CLF) in Statistical Energy Analysis (SEA) framework, for two degree of freedom vibration system. The aim of this study is to obtain a new interpretation of the energy transfer viewpoint of the phenomena represented by the two-degree-of-freedom vibration system and to utilize it for vibration control and structural design. In this paper, the energy transfer characteristics of one-degree-of-freedom and two-degree-of-freedom vibration systems are explicitly derived from the equations of motion under broadband excitation through the energy (power) equilibrium and the frequency average concept. And then it is shown that the derived energy transmissibility can be described by a mathematical equation and is a single value. The transmissibility is determined by three parameters, which are the properties concerning each two uncoupled one degree of freedom vibration system, the uncoupled natural angular frequencies, the damping and the coupling properties. Then the energy transmissibility can be easily used for understanding phenomena described by the two degree of freedom systems and for control and design them. Furthermore, as an example of vibration control based on the energy transmissibility, the reduction of engine shake in automobiles is presented, and its effectiveness is compared and discussed with the results in the reference.

Yuanxian Chen, Xing Zheng

In modern education, it has been generally believed that the most important responsibility of educators is to awaken the ability of students. It is a general goal to develop the students’ comprehensive knowledge and practical skills in majoring in animation at universities. Thus, we investigated the effects of learning to understand the social significance of the academic ability and technical skills of graduates. Then, we reviewed the teaching methods in animation and analyzed the uniqueness and complexity of teaching to understand the evolutionary communication method and their complementary resources. We propose a creative method to gradually promote teaching animation. Through the experiments used in the course, the teaching effects were compared. The result of this research provides educators with a reference for developing a method of teaching animation.

Ashok Pomnar, Anand Singh Rajawat, Nisha S. Tatkar et al.

Data localization, data explosion, data security, data protection, and data acceleration are important driving forces in India’s datacenter revolution, which has raised a demand for datacenter expansion in the country. In addition, the pandemic has pushed the need for technology adoption, digitization across industries, and migration to cloud-based services across the globe. The launch of 5G services, digital payments, big data analytics, smartphone usage, digital data access, IoT services, and other technologies like AI (artificial intelligence), AR (augmented reality), ML (machine learning), 5G, VR (virtual reality), and Blockchain have been a strong driving force for datacenter investments in India. However, the rapid expansion of these datacenters presents unique challenges, particularly in predicting and managing their power requirements. This abstract focuses on understanding the power prediction and demand aspects specific to hyperscale datacenters in India. The study aims to analyze historical power consumption data from existing hyperscale datacenters in India and develop predictive models to estimate future power requirements. Factors such as server density, workload patterns, cooling systems, and energy-efficient technologies will be considered in the analysis. Datacenter negatively impacts the environment because of the large consumption of power sources and 2% of the global contribution of greenhouse gas emissions. Given the increasing cost of power, datacenter players are naturally encouraged to save energy, as power is a high datacenter operational expenditure cost. Additionally, this research will explore the impact of renewable energy integration, backup power solutions, and demand–response mechanisms to optimize energy usage and reduce reliance on conventional power sources. Many datacenter providers globally have started using power from renewable energy like solar and wind energy through Power Purchase Agreements (PPA) to reduce these carbon footprints and work towards a sustainable environment. In addition, today’s datacenter industry constantly looks for ways to become more energy-efficient through real innovation to reduce its carbon footprint.

Enric Dorca, Daniel Bonet-Solà, Pau Bergadà et al.

The lockdown in Spain due to COVID-19 caused a strong decrease in the urban noise levels observed in most cities, clearly followed in the case that these cities had acoustic sensor networks deployed. This fact had an impact on people’s lives, who, at that time, were mainly locked at home due to health reasons. In this paper, we present a qualitative analysis of the subjective vision of the citizens participating in a data-collecting campaign during the COVID-19 lockdown in Girona, a Catalan city, named ‘Sons al Balcó’. The alignment of the subjective data gathered was too scarce to conduct final conclusions, but already giving a bias of the results indicates that the objective <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mi>A</mi><mi>e</mi><mi>q</mi></mrow></msub></semantics></math></inline-formula> measurements, which showed a clear decrease in noise in the streets during the lockdown, were supported by the fact that new sounds found during the lockdown were not very annoying. Former existing noise sources, such as road traffic noise or leisure noise, are depicted as annoying but their decrease during the lockdown improved the soundscape of many homes. This paper’s goal is to show the possibility of gathering both objective and calibrated data with perceptive approximation for the first time in ‘Sons al Balcó’, and how this supports our conclusions, in survey with a limited number of participants conducted during the 2020 lockdown period in Catalonia.

Tao Chen, Miqing Li

Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.

Amit Seal Ami, Syed Yusuf Ahmed, Radowan Mahmud Redoy et al.

While software engineers are optimistically adopting crypto-API misuse detectors (or crypto-detectors) in their software development cycles, this momentum must be accompanied by a rigorous understanding of crypto-detectors' effectiveness at finding crypto-API misuses in practice. This demo paper presents the technical details and usage scenarios of our tool, namely Mutation Analysis for evaluating Static Crypto-API misuse detectors (MASC). We developed $12$ generalizable, usage based mutation operators and three mutation scopes, namely Main Scope, Similarity Scope, and Exhaustive Scope, which can be used to expressively instantiate compilable variants of the crypto-API misuse cases. Using MASC, we evaluated nine major crypto-detectors, and discovered $19$ unique, undocumented flaws. We designed MASC to be configurable and user-friendly; a user can configure the parameters to change the nature of generated mutations. Furthermore, MASC comes with both Command Line Interface and Web-based front-end, making it practical for users of different levels of expertise.

Sean McGuire, Erin Shultz, Bimpe Ayoola et al.

Background: Sustainable software engineering (SSE) means creating software in a way that meets present needs without undermining our collective capacity to meet our future needs. It is typically conceptualized as several intersecting dimensions or ``pillars'' -- environmental, social, economic, technical and individual. However; these pillars are theoretically underdeveloped and require refinement. Objectives: The objective of this paper is to generate a better theory of SSE. Method: First, a scoping review was conducted to understand the state of research on SSE and identify existing models thereof. Next, a meta-synthesis of qualitative research on SSE was conducted to critique and improve the existing models identified. Results: 961 potentially relevant articles were extracted from five article databases. These articles were de-duplicated and then screened independently by two screeners, leaving 243 articles to examine. Of these, 109 were non-empirical, the most common empirical method was systematic review, and no randomized controlled experiments were found. Most papers focus on ecological sustainability (158) and the sustainability of software products (148) rather than processes. A meta-synthesis of 36 qualitative studies produced several key propositions, most notably, that sustainability is stratified (has different meanings at different levels of abstraction) and multisystemic (emerges from interactions among multiple social, technical, and sociotechnical systems). Conclusion: The academic literature on SSE is surprisingly non-empirical. More empirical evaluations of specific sustainability interventions are needed. The sustainability of software development products and processes should be conceptualized as multisystemic and stratified, and assessed accordingly.

Sarat Chandra Patra, Sumit Swain, Pragyan Senapati et al.

The authors developed a nanocomposite using polypropylene (PP) and graphene nanoplatelets (GNPs) with a melt mixing method. Virgin PP was filled with three sets of GNPs with a fixed thickness (15 nm) and surface area (50–80 m²/g). The selected H-type GNPs had three different sizes of 5, 15 and 25 µm. The nanocomposites were made by loading GNPs at 1, 2 and 3 wt.%. Mechanical analysis was carried out by performing tensile, flexural and impact strength tests. The crystalline, micro-structural, thermal and dynamic mechanical properties were assessed through XRD, FESEM, PLM, DSC, TGA and DMA tests. It was observed that all three types of GNPs boosted the mechanical strength of the polymer composite. Increasing the nanofiller size decreased the tensile strength and the tensile modulus, increased the flexural strength and flexural modulus, and increased the impact strength. Maximum tensile strength (≈41.18 MPa) resulted for the composite consisting 3 wt.% H5, whereas maximum flexural (≈50.931 MPa) and impact (≈42.88 J/m) strengths were observed for nanocomposite holding 3 wt.% H25. Graphene induced the PP’s crystalline phases and structure. An improvement in thermal stability was seen based on the results of onset degradation (T_D) and melting (T_m) temperatures. Graphene increased the crystallization (T_c) temperatures, and acted like a nucleating agent. The experimental analysis indicated that the lateral size of graphene plays an important role for the nanocomposite’s homogeneity. It was noted that the small-sized GNPs improved dispersion and decreased agglomeration. Thus overall, small-sized GNPs are preferable, and increasing the lateral size hardly establishes feasible characteristics in the nanocomposite.

L. A. Bull, D. Di Francesco, M. Dhada et al.

A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different sub-groups, representing (i) use-type, (ii) component, or (iii) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet and power prediction in a wind farm. In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets share correlated information at different levels of the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e. parameter). Both case studies demonstrate the wide applicability to practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.