Large Machines Research Articles

FROM THE TRADITIONAL MULTILATERATION TO THE INTEGRATED MULTILATERATION APPROACH: A ROADMAP TOWARDS THE AUTOMATIZATION OF VOLUMETRIC ERROR MAPPING PROCESS FOR LARGE MACHINE TOOLS

Multilateration-based volumetric error mapping is gaining widespread adoption in the coordinate measurement machine and machine tool industry as the most effective approach for geometric characterization of large-size machines. In the traditional method, tracking interferometers are placed on the machine table while the measurement retro-reflector moves with the spindle. However, this approach has several limitations, including manual instrument manipulation and a sequential measurement scheme that hinders unattended execution. To overcome these limitations, the integrated multilateration method has been introduced, which addresses these challenges and reduces measurement uncertainty. This method offers the potential for fully autonomous volumetric error mapping of large-size machines and enables the development of new functionalities such as traceable coordinate measurement with large machine tools. This research presents the roadmap followed by the authors to automate the volumetric error mapping process for large machine tools. It outlines the current status of the technology, starting from the conceptual development and culminating in the experimental results obtained from real industrial machine tools.

PROCESS CONTROL COMBINING MACHINE LEARNING AND FINGERPRINT APPROACHES

Manufacturing operations in large machine tools often requires several hours per part. Ensuring output quality is vital to avoid time and financial losses. While quality assurance was always problematic and costly, the recent advent of Industry 4.0 brought a new perspective to the problem as cutting machines are now fully digitized. This paper proposes a process control framework that combines a fingerprint approach that detects deviations with respect to the validated process and a Long Short-Term Memory (LSTM) algorithm that predicts the upcoming signals. This paper demonstrates how combining these two methodologies surpasses the performance of previous purely learning-based algorithms.

Challenges of Large Converter-Fed Synchronous Machines for Variable-Speed Pumped Hydro Storage

The green energy transition of electrical energy production is leading to an increasing share of total energy production for volatile renewable energy sources, mainly wind and solar power. To handle this volatile production, flexible and efficient energy storage is required. The development of high-power converters has enabled the generation of variable-speed pumped hydro storage power plants, combining the so-far-unequalled energy storage capacity of classical pumped-storage hydro power plants and the recently increased operation requirements. The introduction of large-scale converters has led to new challenges in the overall design of power plant systems. This paper intended to take a closer look at a large-scale converter-fed synchronous generator, especially the distribution of the current and voltage harmonics caused by the converter in the implemented generation system. Thereby, holistic design considerations for an ideal loss distribution as well as possible measures to limit the effects of harmonic coupling at the generator shaft and bearings are discussed. Furthermore, basic considerations of harmonic emission to the connected network are described. These topics are addressed by analyzing on-site measurements at an 85 MVA converter-fed synchronous generator with a voltage source inverter, underpinned with the theoretical background.

Fuzzy large margin distribution machine for classification

Fuzzy large margin distribution machine for classification

WEEGIT – software for visualization and annotation of the electrophysiological activity registration data

Wizard for EEG Information Tabling – “WEEGIT” is a software designed for visualization and annotation of the long-lasting records of activity. Long-lasting records of raw data are used in various areas of life sciences and well represented in neuroscience. In spite of development of other techniques and approaches the electrophysiological activity registration remains the “golden standard” in neurobiological research. Therefore, we developed and verified “WEEGIT” as a powerful and convenient tool for electrophysiological recordings’ description. We combined the most important features presented in other commercial and noncommercial software in the “WEEGIT”. Presented software can operate with widely used formats of records. ”WEEGIT” allows adaptive visualizing of up to 512 channels of record in different timescale without loss of efficiency and consumption of large machine resources. Visualizing also includes optional built-in temporal-spatial analysis (density of current sources or the density of action potentials) displayed as a background image. Integration of a set of optional filtering and signal transforming procedures allows improving record visualization. “WEEGIT” has a convenient graphical user interface with opportunity of simultaneous time browsing and annotating of the records in one workspace. Annotation can be done by simple text information typing as well as an interactive placing of specialized labels and objects. Software also allows saving data in the resampled format, both for the whole record and for user-defined events. In conclusion, “WEEGIT” provides a great set of benefits for the convenient workflow for beginners and specialists in the electrophysiological area of research, including preparation of the data for further specialized analysis.

AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric

This article presents a solid color circular weft fabric defect detection method based on AYOLOv7-tiny. The aim of the development of this network is to enable real-time defect detection in the production of large circular machine fabrics. The network has a good accuracy rate, fast detection speed, and a lightweight model. In the YOLOv7-tiny network, a space-to-depth layer followed by a non-strided convolution layer is introduced to enhance the feature extraction capability, improve image sharpness, address issues such as uneven grayscale and difficult detection of minor defects, and simplify the model complexity while reducing computation. Additionally, we combined the Squeeze and Excitation (SE) and Spatial Attention Module (SAM) based on Convolutional Block Attention Module (CBAM) to construct the Hybrid Attention Module (SC), which is integrated into the YOLOv7-tiny network. The SC module increases the weight of important features, enhances the feature extraction capability, improves image segmentation, and enhances the accuracy of the network's location information. Through extensive experiments with a dataset of large circular machine solid color circular weft fabric defect collected from an industrial site, the results show that AYOLOv7-tiny has a detection accuracy (mean average precision) of 98.7%, a detection speed of up to 333 frames per second, and a computational complexity of only 4.4 GFlops, which is better than the current mainstream surface defect detection models. The detection accuracy, detection speed, and model complexity of the AYOLOv7-tiny network all meet the real-time detection requirements of the industry and have been successfully used for real-time detection of large circular machine fabric defects.

Translation Performance from the User’s Perspective of Large Language Models and Neural Machine Translation Systems

The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI’s ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes.

Privacy-Preserving Federal Learning Chain for Internet of Things

The expansion of Internet of Things (IoT) spawns large on-device machine learning demands, while the machine learning can be a hard task for resource constrained IoT terminals with fragmented dataset. Federal learning (FL), which aims to build a joint model across multiple devices, IoT-FL has now become a promising path for learning on terminals. In broad FL fields, current server-client pattern cannot jump out of the third party self-trustless problem, and recent researches suggest that even sharing training results may also reveal the raw data sets. Homomorphic encryption (HE) is a powerful method in privacy preserving, while so far it is hard to applied HE into multi-party computing (MPC) scenarios including FL. Combining with the existing IoT architecture, in this paper, we customize a scheme (FL chain) dedicated for the privacy and trustiness issues in IoT-FL scenarios, which integrates blockchain smart contract and HE. Differ from traditional schemes, our FL chain is highly adaptive with current IoT architecture and it is the first scheme that applied HE into IoT-FL privacy preserving. Theoretical analysis and experimental results prove the feasibility of FL chain.

The importance of gravity vector on adult mammalian organisms: Effects of hypergravity on mouse testis.

In the age of space exploration, the effect of hypergravity on human physiology is a relatively neglected topic. However, astronauts have several experiences of hypergravity during their missions. The main disturbance of altered gravity can be imputed to cell cytoskeleton alteration and physiologic homeostasis of the body. Testis has proved to be a particularly sensible organ, subject to environmental alteration and physiological disturbance. This makes testis an organ eligible for investigating the alteration following exposure to altered gravity. In our study, mice were exposed to hypergravity (3g for 14 days) in the Large Diameter Centrifuge machine (ESA, Netherland). We have observed a morphological alteration of the regular architecture of the seminiferous tubules of testis as well as an altered expression of factors involved in the junctional complexes of Sertoli cells, responsible for ensuring the morpho-functional integrity of the organ. The expression of key receptors in physiological performance, such as Androgen Receptors and Interstitial Cells Stimulating Hormone receptors, was found lower expressed. All these findings indicate the occurrence of altered physiological organ performance such as the reduction of the spermatozoa number and altered endocrine parameters following hypergravity exposure.

LE-YOLOv5: A Lightweight and Efficient Road Damage Detection Algorithm Based on Improved YOLOv5

Road damage detection is very important for road safety and timely repair. The previous detection methods mainly rely on humans or large machines, which are costly and inefficient. Existing algorithms are computationally expensive and difficult to arrange in edge detection devices. To solve this problem, we propose a lightweight and efficient road damage detection algorithm LE-YOLOv5 based on YOLOv5. We propose a global shuffle attention module to improve the shortcomings of the SE attention module in MobileNetV3, which in turn builds a better backbone feature extraction network. It greatly reduces the parameters and GFLOPS of the model while increasing the computational speed. To construct a simple and efficient neck network, a lightweight hybrid convolution is introduced into the neck network to replace the standard convolution. Meanwhile, we introduce the lightweight coordinate attention module into the cross-stage partial network module that was designed using the one-time aggregation method. Specifically, we propose a parameter-free attentional feature fusion (PAFF) module, which significantly enhances the model’s ability to capture contextual information at a long distance by guiding and enhancing correlation learning between the channel direction and spatial direction without introducing additional parameters. The K-means clustering algorithm is used to make the anchor boxes more suitable for the dataset. Finally, we use a label smoothing algorithm to improve the generalization ability of the model. The experimental results show that the LE-YOLOv5 proposed in this document can stably and effectively detect road damage. Compared to YOLOv5s, LE-YOLOv5 reduces the parameters by 52.6% and reduces the GFLOPS by 57.0%. However, notably, the mean average precision (mAP) of our model improves by 5.3%. This means that LE-YOLOv5 is much more lightweight while still providing excellent performance. We set up visualization experiments for multialgorithm comparative detection in a variety of complex road environments. The experimental results show that LE-YOLOv5 exhibits excellent robustness and reliability in complex road environments.

Exploring Composite Indexes for Domain Adaptation in Neural Machine Translation

Domain adaptation in neural machine translation (NMT) tasks often involves working with datasets that have a different distribution from the training data. In such scenarios, k-nearest-neighbor machine translation (kNN-MT) has been shown to be effective in retrieving relevant information from large datastores. However, the high-dimensional context vectors of large neural machine translation model result in high computational costs for distance computation and storage. To address this issue, index optimization techniques have been proposed, including the use of inverted file index (IVF) and product vector quantization (PQ), called IVFPQ. In this paper, we explore the recent index techniques for efficient machine translation domain adaptation and combine multiple index structures to improve the efficiency of nearest-neighbor search in domain adaptation datasets for machine translation task. Specifically, we evaluate the effectiveness when combining optimized product quantization (OPQ) and hierarchical navigable small-world (HNSW) indexing with IVFPQ. Our study aims to provide insights into the most suitable composite index methods for efficient nearest-neighbor search in domain adaptation datasets, with a focus on improving both accuracy and speed.

A Novel Dual-Center-Based Intuitionistic Fuzzy Twin Bounded Large Margin Distribution Machines

Intuitionistic fuzzy (IF) set theory combined with twin support vector machines (TSVM) has shown highly advantageous performance in robust and fast classification. However, the existing methods have some inherent flaws: i) The inaccurate confidence caused by the single-center based IF strategy degrades the classification performance of the model. ii) The non-membership degree is time-consuming and the threshold is difficult to determine, as it depends on sample's local neighborhood set. iii) Recent theoretical studies have demonstrated that optimizing the margin distribution achieve better generalization performance than maximizing the minimum margin in SVM-based models (SVMs). In view of the aforementioned shortcomings, we propose a novel dual-center based IF (DC-IF) function, and introduce the margin distribution into IFTSVM, forming a DC-IF twin bounded large margin distribution machines (DC-IFTBLDM). Initially, the DC-IF strategy references the contribution of one sample to both categories, which is time efficient and more accurately reflects the credibility of the samples. Moreover, the margin mean and variance are used to optimize the margin distribution, and a regularization term is utilized to minimize structural risk. Therefore, DC-IFTBLDM has a good performance in noise immunity and classification capability. Comparative experiments based on synthetic datasets, UCI benchmark datasets, and datasets with different noise ratios verify the effectiveness of the proposed model. The parameter sensitivity analysis further proves the high accuracy and stability of IFTBLDM.

Reviews on the machining and measurement of large components

The study summarized the characteristics of large board and shaft components. The high-end large machine equipment and clamping devices should be developed in machining for high requirements. Additionally, some non-conventional methods have became a increasing for large components. For measurements, the developing and advanced methods were summarized, and the compensation methods were also complement to realize the high precision and efficiency. If that the component’s size has greatly exceeded the range of the current equipment, the mobile equipment are recommended for machining and measurement, and its detection technology is developed essentially for multiple mobile ones. Thus the study has guiding significance for achieving the high-precision and efficiency machining and measurement for large components, which is a great need in related industries.

Emulation of IEEE STD 421.5/Industrial Excitation Systems Using a Micro-Alternator's Exciter

Micro-alternators, having similar parameters as turbo-alternators, play a significant role in the experimental evaluation of power system dynamics. Early thyristor excitation systems of micro-alternators have limited re-configurable options. Three possible control structures, two closed-loop control structures proposed in the literature and one open-loop control structure proposed in this paper, including a time constant regulator to mimic large machine time constants, are evaluated to emulate any IEEE 421.5 standard excitation system model or interface an industrial excitation system controller hardware in the loop (CHIL). It is shown that the communication delay introduced in CHIL does not impact the closed-loop stability within the bandwidth requirements of the excitation systems. One of the closed-loop structures, which is more generalized, and the open-loop structure performances are compared for emulating IEEE AC4C, DC1C, and ST1C excitation systems on a laboratory micro-machine based single machine infinite bus test system with buck exciter and H-bridge chopper (HBC) exciter using EMTP simulations under small and large disturbances. HBC exciter is used in the experimental validation with both the closed-loop and the open-loop control structures. The proposed open-loop structure is easy to implement and performs better than the closed-loop structure.

Mitochondrial microproteins: critical regulators of protein import, energy production, stress response pathways, and programmed cell death.

Mitochondria rely upon the coordination of protein import, protein translation, and proper functioning of oxidative phosphorylation (OXPHOS) complexes I-V to sustain the activities of life for an organism. Each process is dependent upon the function of profoundly large protein complexes found in the mitochondria [translocase of the outer mitochondrial membrane (TOMM) complex, translocase of the inner mitochondrial membrane (TIMM) complex, OXPHOS complexes, mitoribosomes]. These massive protein complexes, in some instances more than one megadalton, are built up from numerous protein subunits of varying sizes, including many proteins that are ≤100-150 amino acids. However, these small proteins, termed microproteins, not only act as cogs in large molecular machines but also have important steps in inhibiting or promoting the intrinsic pathway of apoptosis, coordinate responses to cellular stress, and even act as hormones. This review focuses on microproteins that occupy the mitochondria and are critical for its function. Although the microprotein field is relatively new, researchers have long recognized the existence of these mitochondrial proteins as critical components of virtually all aspects of mitochondrial biology. Thus, recent studies estimating that hundreds of new microproteins of unknown function exist and are missing from current genome annotations suggests that the mitochondrial "microproteome" is a rich area for future biological investigation.

Assessment of the Performance of selected Special Care Newborn Units (SCANUs) in Bangladesh: A cross Sectional Study

Background: Government of Bangladesh initiated the Special Care Newborn Units (SCANU) in 2011 at selected district hospitals followed by its scale up in other districts with support from UNICEF and other partners. The objective of the study was to assess the performance of those SCANUs in Bangladesh. Methods: A cross-sectional study approach was applied for assessment of the performance of SCANU facilities compared to facilities without SCANU. Fifteen facilities with SCANUs and five facilities without SCANUs were selected from eight divisions of the country for the study. The selected SCANU and Non-SCANU facilities were of secondary level hospital with similar patient flow and workload. Quantitative data were collected from direct observation of the 20 facilities and interviews with 678 caregivers using a field-tested questionnaire. Caregivers were mothers of the infants (<59 days old) who were admitted and stayed for at least one day in those hospitals. Analysis was done using Stata version 15.1 and presented in tabular, graphical and numerical measures of central tendency and dispersion. Result: The results of the assessment found that 60% of SCANU facilities had KMC care services and 27% of SCANU facilities had rooming in services and special sick baby ward services but no such services was available in non-SCANU facilities. Equipment in the SCANUs such as resuscitation bag and mask, radiant warmers, phototherapy machines and to a large extent CPAP machines and Oxygen blenders were mostly available and functioning optimally. More than half of the SCANUs still need to have oxygen blenders, CPAP machines and transcutaneous bilirubinometers since they were not found/functional during the assessment. Majority of SCANU facilities 60%-80% reported no stock-outs of critical medications like dexamethasone, ceftriaxone and gentamicin which are critical for management of preterm and sick babies. A web-based online ‘Individual Case Tracking’ system has been developed for online reporting from all SCANUs by the Health Management Information System (HMIS) of the Directorate General of Health Services (DGHS). UNICEF was supporting its linkage to the district health information system (DHIS-2). There was a statistically significant difference between SCANUs and non-SCANUs with respect to the proportions of admissions. SCANUs are acting as referral destination for sick and small newborns as 41%-45% of the admitted newborns were either were born at home or in other facilities. Conclusion: The assessment findings will be helpful for country strategy for improving newborn care through expansion of SCANU expansion throughout the country

A Novel Cross-Sensor Transfer Diagnosis Method with Local Attention Mechanism: Applied in a Reciprocating Pump.

Data-driven mechanical fault diagnosis has been successfully developed in recent years, and the task of training and testing data from the same distribution has been well-solved. However, for some large machines with complex mechanical structures, such as reciprocating pumps, it is often not possible to obtain data from specific sensor locations. When the sensor position is changed, the distribution of the features of the signal data also changes and the fault diagnosis problem becomes more complicated. In this paper, a cross-sensor transfer diagnosis method is proposed, which utilizes the sharing of information collected by sensors between different locations of the machine to complete a more accurate and comprehensive fault diagnosis. To enhance the model's perception ability towards the critical part of the fault signal, the local attention mechanism is embedded into the proposed method. Finally, the proposed method is validated by applying it to experimentally acquired vibration signal data of reciprocating pumps. Excellent performance is demonstrated in terms of fault diagnosis accuracy and sensor generalization capability. The transferability of practical industrial faults among different sensors is confirmed.

Site-Specific Introduction of Alanines for the Nuclear Magnetic Resonance Investigation of Low-Complexity Regions and Large Biomolecular Assemblies.

Nuclear magnetic resonance (NMR) studies of large biomolecular machines and highly repetitive proteins remain challenging due to the difficulty of assigning frequencies to individual nuclei. Here, we present an efficient strategy to address this challenge by engineering a Pyrococcus horikoshii tRNA/alanyl-tRNA synthetase pair that enables the incorporation of up to three isotopically labeled alanine residues in a site-specific manner using in vitro protein expression. The general applicability of this approach for NMR assignment has been demonstrated by introducing isotopically labeled alanines into four distinct proteins: huntingtin exon-1, HMA8 ATPase, the 300 kDa molecular chaperone ClpP, and the alanine-rich Phox2B transcription factor. For large protein assemblies, our labeling approach enabled unambiguous assignments while avoiding potential artifacts induced by site-specific mutations. When applied to Phox2B, which contains two poly-alanine tracts of nine and twenty alanines, we observed that the helical stability is strongly dependent on the homorepeat length. The capacity to selectively introduce alanines with distinct labeling patterns is a powerful tool to probe structure and dynamics of challenging biomolecular systems.

Theoretical and Experimental Verification of CVR Energy Savings for Refrigeration Loads

This paper presents analytical and experimental studies on Conservation Voltage Reduction (CVR) for Refrigeration Loads (RLs) driven by single-phase and three-phase induction motors. It is shown that the increase in motor efficiency is the factor that determines the regions where CVR works for RL loads. The results of this paper dispel the belief that CVR cannot save energy from RLs because the output energy must be constant to keep the same temperature. A criterion is developed to assess the effectiveness of CVR for RL loads. It is shown theoretically and experimentally that CVR energy savings come from the reduction of iron-core losses. Traditionally, the iron core losses are represented by shunt resistances ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_m</i> ) that are frequently neglected in the literature. However, they are essential for the proper evaluation of CVR. Induction machines of all ratings have a region where energy savings can be obtained. When operating near their rating smaller machines produce larger energy savings under reduced voltages than larger machines in proportion to their ratings. An additional benefit of CVR, when applied to motor loads, is that the power factor increases substantially.

Glass formulation and composition optimization with property models: A review

AbstractGlass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.