bluuebunny/crossref_metadata_2025 huggingface dataset:
An Innovative Interface Design and Customized Usability Testing Method: Case Study of Internet of Things Integration Platform Interface
Authors: Wu Jia-Jiu  , Chiu Ming-Chuan
Published: February 2025

User Interface (UI) plays a critical role to a successful product, since UI provides the first impression to users when they use the product. UI will directly affect the experience of customers, and then influence the satisfaction of the product. Despite an interface includes a large amount of information and complex processes, the current interface design method rarely concerns about the aspects of the process and information capacity. This results in bad accessibility. In addition, the existing usability methods fail to customize criteria for different types of the interface. Therefore, this study proposed an IDEF (ICAM DEFinition Language) based interface design method along with a customized usability test method which is called ANP (Analytic Network Process) to calculate the weights of usability metrics so as to better assess the designed interfaces. A case study of IOT (Internet of Things) platform website UI will be provided to demonstrate the advantage of the proposed method, which can reduce the development time of UI and improve user satisfaction.
Development of System to Support Knowledge Discovery in Historical Study with Linked Data
Authors: Nakamura Satoru  , Hiekata Kazuo  , Mitsuyuki Taiga  , Kato Satoshi  , Miyamoto Takashi  , Takashima Tomoko
Published: February 2025

In the field of historical study, the system called Digital Archive, which digitizes historical resources and publish them on the web, has been developed. This helps preventing damage of resources and providing them beyond temporal and geographical constraints. On the other hand, practical use of Digital Archive has not been considered, because the primary aim of it is preservation and publishing of them. This prevents from sharing data and knowledge with others and efficient usage of published data such as digitalized images and catalog data. As for this problem, Linked Data has been considered as one of solutions. Linked Data is a term used to describe a practice for sharing and connecting data and knowledge on the web. The objective of this paper is to develop the system to support knowledge discovery in historical study by use of Linked Data. Developed system connects with Digital Archive through Linked Data, and allowed historians to use catalog data on the web and research data, which historians accumulate based on their objectives, integrally. This helps historians to analyze a large amount of historical resources quantitatively, and encourages to discover new knowledge which have not been discovered by qualitative analysis. We apply developed system to the case of historical study, and the effectiveness was verified by finding new characteristics of historical resources.
Trans-Disciplinary Systems as Complex Systems
Authors: Wognum Nel  , Verhagen Wim J.C.  , Stjepandić Josip
Published: February 2025

The system concept is a widely-used concept in research and practice. Already in the 50s of the previous century, a community was created to investigate interrelationships between domains and create a theory surpassing and comparing domains. The General Systems Theory (GST) community has tried to come up with such a theory for several decades. The ambition has grown more realistic in the years after, recognizing that an all-encompassing theory would not be possible. Since then, systems research was aimed at generating useful and usable approaches to compare and interrelate domains, thus creating a trans-disciplinary approach to enable description and analysis of large, and even complex, systems. The concept of systems, however, is often loosely used. Levels of abstraction are neglected, and interrelationships between systems ignored. In this paper, the concept of system is put in historical context, and further elaborated upon in the context of complex and trans-disciplinary systems. Two examples of transdisciplinary systems are presented and discussed to illustrate the use of the system concept.
Research on Form Attractiveness of Electric Vehicle
Authors: Xi Le  , Cheng Jianxin  , Wu Yixiang  , Ye Junnan  , Xiao Wangqun
Published: February 2025

Electric vehicle (BEV) is different from the traditional vehicle with technology and structure. It has high creative freedom of the appearance design. However, whether this new concept can meet user preferences is a common problem which is also facing by designers. Research methods of Miryoku Engineering can effectively deal with the emotion evaluation between user preferences and product attractiveness. This study analyzed the user preferences of BEV form characteristics through the Evaluation Grid Method (EGM) and the Quantification Theory Type I (QT-1), captured the effective form attractiveness and design factors which come from the users' perceptual, and put forward the conceptual design method of attractiveness factors. Through the Miryoku Engineering application, this study aims to explore a more reliable method to enhance customer satisfaction of BEV in appearance design.
An Empirical Study of the Social E-Commerce Services Model in Taiwan
Authors: Wang Chien-Chih  , Hsieh Hsin-Ling
Published: February 2025

In recent years, the rise of social media has driven business opportunities. This study explored the economy of closed social media fan groups, which represent a type of customer-to-customer (C2C) model. On the basis of the analysis of the current situation, we propose a three-step process that sets up the integration models for (1) the channel and stock, (2) supplier orders, and (3) consumers; compare As-Is and To-Be logistics on the one hand and cash flows and information flows on the other; and offer corresponding plans and suggestions.
Estimating Cost of New Products Using Fuzzy Case-Based Reasoning and Fuzzy Analytic Hierarchy Process
Authors: Kasie Fentahun M.  , Bright Glen  , Walker Anthony
Published: February 2025

Cost estimation is one of the influencial requirements for the current manufacturers when new products are introduced. This paper aims to propose a decision support system (DSS) that retrieves historical cases/products, which have the most similar cost estimates to the current case. This helps users to estimate the costs of new products at early stages of product development. The proposed DSS combines case-based reasoning (CBR), the analytic hierarchy process (AHP) and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. A numerical example is illustrated to show the applicability of the proposed DSS.
Optimising Material Property Testing of ULTEM 9085, a High Performance Additive Manufacturing Material, for Efficient Component Design
Authors: Rehman Fayyaz  , Diston John
Published: February 2025

Additive manufacturing (AM) or 3D printing, is seen amongst some technologists, designers and engineers as the “second industrial revolution”. This is because of the advantages AM can bring to product design and development and with the advancement of AM machine technology, the dream of it one day replacing some conventional manufacturing techniques may slowly become a reality. However there are many challenges to overcome before then, these include machine and material costs, production speed, attitudes towards new, often expensive AM technologies and a lack of reliable material design data. This paper will outline a solution to the lack of available material design data through a material characterization regime that will enable efficient component design, also enhancing how AM is perceived and promoting its adoption as a viable manufacturing technique. The development of the material characterization regime focused on ULTEM 9085, determining which material properties were required, ensuring efficient dimensional measurement and test methods were used, before the manufacture and testing of tensile and compressive samples. Finally a finite element analysis (FEA) truth test was performed to assess the reliability of the data produced.
Building in Multifunctionality in Plastic Components: Complexity, Cost and Sustainability
Authors: Goodship Vannessa  , Middleton Bethany  , Fiamegkou Eleni  , Cherrington Ruth
Published: February 2025

Multifunctionality can be embedded into material systems by three distinct design processes. These are: firstly multifunction can be embedded at a material level such as the use of nanomaterials within a polymer. In the second instance, discrete material systems can be added together. Examples are laminate systems in food pouches consisting of thin layers of metal and polymer. In the third process this can be achieved by integrating these materials systems together to form one holistically behaving component with multifunctionality. An example is an embedded antenna in an automotive windscreen.Drivers for multifunctionality include the increased push towards intelligent objects, such as the creation of the internet of things. Here, the embedding of communication and electronic function into daily consumer objects, such as milk cartons and food packaging are demanded. This must be offset by consideration of the related rise of a new wave of short-lifetime waste electronic and electronic equipment, incapable with current plastic recycling infrastructure, for disposal systems to adapt too. Designing integrated and multifunctional plastic components however, is complicated by the sheer number of material choices, multiple processing platforms, cost implications and environmental legislation. Considering just the processes of injection moulding, compression moulding and additive manufacturing, a designer is confronted with considerable complexity and numerous engineering design and stakeholder issues to consider. This paper presents examples of current state of art in multifunctional systems and discusses the barriers and potential solutions to creating fully realized multifunctional systems within a polymeric manufacturing environment. Impacts on material lifecycles and disposal infrastructures must be considered, as is the necessity to retain diversity with new integrated and advanced manufacturing processes suitable for the demands of mass customization, automation and Industry 4.0.
Eco Lean Sustainability Control Panel – A Tailored Indicator System for Day-to-Day Operations in Manufacturing Facilities
Authors: Miehe Robert  , Bogdanov Ivan  , Sauer Alexander
Published: February 2025

Eco Lean Management aims at simultaneously improving the economic and ecologic performance of manufacturing systems. Appropriate decision making at the business level requires particularly filtered information for both day-to-day operation and long term strategic alignment. In this paper we thus present a control panel consisting of 18 aggregated indicators supporting decision making for low cost economic and ecologic optimization. Additionally, we provide a system to identify interdependencies as a function of strategic goals and discuss its application from a strategic and operational perspective according to the maturity of a company. We conclude with an example of a job shop production environment at a small manufacturing company.
Improved Integration Between Engineering and Supply Chain Through a Data Quality and Governance Framework
Authors: Sanders Chris
Published: February 2025

Within a complex design and build programme the relationship between business disciplines underpins the development of the product. Such programmes have had a history of problems integrating supply chain and engineering information, which has contributed to cost and schedule impact. Through BAE Systems Naval Ships Product Lifecycle Management approach, a Data Quality and Governance framework has been implemented to reduce such issues through information cataloguing and data quality measurement. Mapping product data processes allows the elimination of disparate data sources and directs users to a single point of truth that serves the needs of all stakeholders. In addition, cross functional working groups are being used to develop new data exchange practices that maintain data integrity. Ultimately this will ensure that arriving supplier equipment will be correctly identified for planned installation and supplier data is tracked and stored consistently. This paper will describe this novel approach and the challenges experienced implementing it on a major shipbuilding programme.
A PLM Centric Approach to the Transition from Design to Manufacturing in First of Class Naval Shipbuilding
Authors: Arthur Steven
Published: February 2025

BAE Systems Naval Ships (NS) is currently delivering the Type 26 Global Combat Ship (T26) for the Royal Navy. In 2017 the T26 programme will transition from the design phase to the manufacturing phase. NS has implemented a novel approach to this transition through its investment in Product Lifecycle Management (PLM). This paper describes the benefits realised as a result of taking a PLM centric approach in this transition over a traditional CAD centric approach. The data management system architecture and PLM system capability developed to enable NS to take this approach are also discussed.
A.Q. Khan: Self-Radicalization of a Scientist
Authors: Katz Jonathan
Published: February 2025

A.Q. Khan was at the center of a multinational nuclear proliferation program, responsible for two successful nuclear weapons programs (those of Pakistan and North Korea), one nascent program (Iran's) and one dismantled program (Libya's). In contrast to more recent terrorists, he was intelligent and educated in a serious technical subject (metallurgy). Nor was he radicalized by external agents. His motives appear to have been personal and nationalist rather than religious. Measures designed to detect Islamist radicalization are unlikely to be effective against future A.Q. Khans; traditional counter-espionage vigilance is required. Even more important, we must recognize that technical training is a strategic good, and its export must be controlled; without a corpus of technically trained people, even the fruits of successful espionage are of little effect.
Examining the Narratives of Radical Islamists and Other Extremely Violent Groups: The Case of the “Islamic State”
Authors: Marone Francesco
Published: February 2025

This chapter examines the narratives of radical Islamists and other extremely violent groups. In particular, the focus is on groups that rely, partially or exclusively, on terrorism to achieve their political ends. Terrorism can be seen as a combination of violence and communication. Narratives are often a crucial part of this communication activity. A useful perspective to approach this question is represented by framing theory. This well-established and sophisticated perspective has been widely used to study, in particular, social movements. However, it can also be fruitfully applied to the analysis of terrorist organizations and militants. A key element in this line of research is the distinction of three “core framing tasks”: “diagnostic” framing (concerning the problem), “prognostic” framing (concerning the solution), and “motivational” framing (concerning the “call to arms”). On the basis of this tripartition, the work explores the narrative of the so-called Islamic State (IS), the more influential and dangerous terrorist organisation of our age.
Educational Outreach to Combat Radicalisation of Scientists: A Study of the Aum Shinrikyo Cult
Authors: Thornton Michael I.
Published: February 2025

Radicalisation of the scientifically well-educated is of concern, as these people may use their knowledge against society by being part of an illegal weapons development programme working on behalf of terrorists or an extreme Cult. A study of the Aum Shinrikyo Cult has been undertaken to understand better how they recruited scientists and to examine what the recruits contributed. Many become radicalised or join cults because they are unemployed, may lack social interaction or a stable family structure, this leading to a sense of frustration, hopelessness and alienation. There is also a similar sense of dissatisfaction and disillusionment amongst some newly graduated scientists, in part due to the falling number of job opportunities for them. The Aum Cult offered a sense of belonging, special privileges and a promise of unlimited research funds in extensive facilities. Although the cult is best known for its chemical weapon attack on the Tokyo underground, they had a nuclear programme and the most extensive non-state biological weapons programme discovered to date. There was a distinct lack of tacit knowledge amongst the recruited scientists and it was in part this ineptitude that contributed to the fact that following an investment of millions of dollars; the cult failed to deliver a true weapon of mass destruction. Although there are programmes of educational outreach related to the dual use aspects of chemistry and biology, they are predominately in English and are to be found in the United States and Europe. More active engagement through outreach needs to be carried out, focusing primarily on the young but also encompassing the educators themselves, with the material translated locally and made available on the Internet. The International Community and, in particular, the educational community has a responsibly to educate and inform scientists as to the dual nature of their work and the consequences to society if they choose to misuse this knowledge.
Automata Based Validation Techniques
Authors: Peled Doron
Published: February 2025

Model checking is used to verify the correctness of digital circuits and code against their formal specification. In case of design or programming errors, it provides a counterexample evidence of erroneous behavior. We describe here model checking and two related techniques: black box checking and runtime verification. Black box checking is a variant of model checking where the description of the system is not given apriory, but we can interface with the system via experiments. Runtime verification checks a current execution against the specification. The common ground for the presentation of these methods is the use of automata theory for both modeling the system and representing the specification.
Establishment of Sustainable Telemedicine Systems in the Balkans and Developing Countries Using IBOT: Initiate-Build-Operate-Transfer Strategy
Authors: Latifi Rifat
Published: February 2025

Background: Establishment of telemedicine programs as tool to help restructuring healthcare system in post-conflict countries as well other developing countries in a sustainable fashion, and to rebuild their health care systems to meet the needs of citizens. Establishing sustainable telemedicine has become a goal of many developing countries around the world. Yet, despite initiatives from a select few individuals and on occasion from various governments, often these initiatives never mature to become sustainable programs.
Telemedicine Experience – Barriers and Challenges in Bulgaria
Authors: Mihova Polina
Published: February 2025

This article is focused on the Bulgarian tendencies and initiatives in the development and application of telemedicine and the importance of implementing evidence-based practice. The paper presents separate descriptive modules, including academic education and experience in telemedicine, the healthcare system in Bulgaria itself and the authors experience in telemedical solutions and implementations.
A Survey of Diversified Domain of Big Data Technologies
Authors: Singh Hari  , Vasuja Richa  , Sharma Reema
Published: February 2025

Big Data is a term used for a large and a variety of data that is structured, unstructured or hybrid. It comes from different sources at a high speed. It is a data that is too big and complicated to store, analyze and interpret. Big data are broadly comprised of 4 Vs: Volume, Velocity, Variety and Veracity; these make it bigger and much diversified domains. The processing, management and analysis of big data has opened-up a number of research domains. This data is different from traditional relational data and so the Relational Database Management Systems (RDBMS) are incapable to handle it. The RDBMS is inadequate to handle the growth in volume of data, heterogeneity of data and need to store and access it speedily. This necessitated a new type of database functionality to support the real time applications and the Big Data Technologies have emerged.
Big Data Analysis in Cloud Using Machine Learning
Authors: Kapoor Lohit  , Sankoju Chandana
Published: February 2025

Data explosion and the tremendous growth in the amount of data that is being generated from various IT services increases an enormous demand for smartly analysing the generated data (structured and unstructured). Since Big data constitutes three V's (Volume, Velocity, and Variety), Machine learning is the best solution for exploiting the information hidden in Big data. With far less dependence on manual direction, it provides various methods and techniques to mine useful information from big and disparate data sources. For better and precise results, Machine learning techniques require a huge volume of data and high computation power. Therefore, to get cost-effective infrastructure Cloud computing is the best option. This chapter is aiming to motivate the beginners, professionals and data analysts to adopt the combination of all these technologies. The product outcomes would help decision makers belonging to different domains in decision-making process. Further, this chapter is exploring state of the art technologies and its benefits on a social/commercial organization.
Application of Big Data Analytics in Cloud Computing via Machine Learning
Authors: Goyal Somya  , Parashar Anubha  , Shrotriya Anita
Published: February 2025

Analysis of big data can be done in many ways. To define trends in Big Data we need to concentrate on the biggest challenges faced by this technology and various strategies have been developed in order to process such large data efficiently. We usually describe this by three factors, popularly known as 3Vs i.e. Volume, Velocity and Variety, which describes most of the features of data. The goal of this chapter is to provide traditional meaning and definitions of big data and how this technology is evolved in order to meet today's requirements and challenges. In this chapter we will describe all the attributes of big data i.e. popularly known as 9Vs. Business Intelligence contains these nine attributes on the basis of statistical models or hypothesis in order to provide better predictions and outcomes for any research or results. Machine Learning provides the platform where the big data analysis can be done using cloud computing. With the help of this chapter academicians, business people and researchers can easily find opportunities as a solution for their required purpose.
A Novel Mechanism for Cloud Data Management in Distributed Environment
Authors: Mishra Kamta Nath
Published: February 2025

In the current era cloud computing is earning very high popularity for its vast characteristics which is divided into its common characteristics and essential characteristics. The common characteristics are massive scale, homogeneity, virtualization, low cost software, resilient computing, geographic computation, service orientation and advanced security. The essential characteristics are on demand self-service, broad network access, resource pooling, rapid elasticity and measured service. Cloud Computing is the internet based computing where the application software, infrastructure and platform are available in the cloud and the end users (businessman, developers) can access it through, as a client. Cloud is a step on from Utility Computing. Owing to increase in use of these services by companies, several security issues have emerged and this challenges cloud computing system to secure, protect and process the data which is the property of user. Therefore, we must develop high level authentication protocols for preventing the security threats.
Performance Analysis of Deadlock Prevention and MUTEX Detection Algorithms in Distributed Environment
Authors: Mishra Kamta Nath
Published: February 2025

In normal multiprogramming environment, several processes may compete for getting resources. Here, if a process requests for a resource and if the resource is not available at a particular time then the process enters into waiting state. But, sometimes a waiting state process is not converted into its present state and thus the process enters into a new situation called as indefinite blocking or deadlock state. Hence, we can say that a deadlock or starvation state occurs in a distributed system whenever two or more processes are in waiting state indefinitely for an event which is caused by one of the waiting processes.
Fading Channel and Imperfect Channel Estimation for OFDM in Wireless Communication
Authors: Jha Sudan  , Son Le Hoang  , Kumar Raghvendra
Published: February 2025

In wireless communication system, the transmitted signal is distorted by various phenomenons that are intrinsic to the structure and contents of the wireless channel. Among these fading and interference has been the main dominant sources of distortion and degradation of performance. Fading can be defined as the fluctuation in amplitude phase and multipath delays over very short travels distance or very short time duration. To increase high data rate in wireless communication Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) techniques have been considered best as they have high frequency spectrum efficiency. In this study, the performance over fading channel on the wireless communication is studied, simulated and compared for Maximum Likelihood (ML) receivers in different modulation structure in the presence of Gaussian Channel estimation error based on different Doppler shift. The second part of this study is to analyse the performance degradation due to channel estimation error, fading noise, and interference between users for several fading channels in multiple users multiple antennas with different performance measures.
MPI Streams for HPC Applications
Authors: Peng Ivy Bo  , Markidis Stefano  , Gioiosa Roberto  , Kestor Gokcen  , Laure Erwin
Published: February 2025

Data streams are a sequence of data flowing between source and destination processes. Streaming is widely used for signal, image and video processing for its efficiency in pipelining and effectiveness in reducing demand for memory. The goal of this work is to extend the use of data streams to support both conventional scientific applications and emerging data analytics applications running on HPC platforms. We introduce an extension called MPIStream to the de-facto programming standard on HPC, MPI. MPIStream supports data streams either within a single application or among multiple applications. We present three use cases using MPI streams in HPC applications together with their parallel performance. We show the convenience of using MPI streams to support the needs from both traditional HPC and emerging data analytics applications running on supercomputers.
Mind, Machine, and Image Processing
Authors: Pandey Subhash Chandra
Published: February 2025

Image processing (IP) and artificial intelligence (AI) is an exciting research area in cognitive and computer science. This chapter deals with the image processing and attempts have been taken to illustrate the intertwining among mind, machine and image processing. Further, different subtle aspects pertaining to mind and intelligence are also presented. In fact, intelligence acts as a substrate of mind to engage it in consciousness. Indeed, mind is distinct from the consciousness and both are subtler than the body. The chapter also elucidates the comparison of mind machine, and intelligence in image processing. Moreover, attempt has been made to envision the core issues related to different aspects like: (1) intelligence is the means to engage the mind in consciousness (2) Brain is analogous to a feed forward hierarchical state machine and acts as a substrate for mind and possesses vital relationship with intelligence. Indeed, there are two important approaches to develop an artificially intelligent device. These are the conceptual understanding of mind and the computational framework of brain. This chapter emphasizes that the agent in which embryonic artificial intelligence is to be created must have the capacity of embodied experience and it must possess sensory components to establish relations between it and the external world. Further, this chapter also renders the philosophical perspectives of some intuitive questions such as: Can mind be explained in terms of machines? Can mind be replicated on machines? Do machines can ever be intelligent? In fact mind or intelligence cannot be reflected by physical means. In addition, this chapter preludes that mainly there are two approaches pertinent to the philosophy of mind. These are the dualism and functionalism. However, it is hard to explain the true nature of mind with the help of only these two approaches because mind displays the characteristics like and dislikes a machine simultaneously. Moreover, the concept of machine seems to be based on dichotomy between these two approaches. Various difficulties pertaining to image processing have also been incorporated.
Hierarchical Object Detection with Deep Reinforcement Learning
Authors: Bellver Bueno Miriam  , Giro-i-Nieto Xavier  , Marques Ferran  , Torres Jordi
Published: February 2025

This work introduces a model for Hierarchical Object Detection with Deep Reinforcement Learning (HOD-DRL). The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention on five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis.We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with a large number of object candidates, the much more reduced number of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions. Source code and models are available at https://imatge-upc.github.io/detection-2016-nipsws/.
Super-Resolution of Long Range Captured Iris Image Using Deep Convolutional Network
Authors: Deshpande Anand  , Patavardhan Prashant P.
Published: February 2025

This chapter proposes a deep convolutional neural network based super-resolution framework to super-resolve and to recognize the long-range captured iris image sequences. The proposed framework is tested on CASIA V4 iris database by analyzing the peak signal-to-noise ratio (PSNR), structural similarity index matrix (SSIM) and visual information fidelity in pixel domain (VIFP) of the state-of-art algorithms. The performance of the proposed framework is analyzed for the upsampling factors 2 and 4 and achieved PSNRs of 37.42 dB and 34.74 dB respectively. Using this framework, we have achieved an equal error rate (EER) of 0.14%. The results demonstrate that the proposed framework can super-resolve the iris images effectively and achieves better recognition performance.
Analysis of Damage's Influences on Metal Magnetic Memory Signal
Authors: Li Hongmei  , Chen Zhenmao  , Zhang Fuchen
Published: February 2025

Metal Magnetic Memory (MMM) technique has been extensively used as a qualitative method to test the position of possible damages in ferromagnetic metal structures, a bottleneck in further development of MMM technique is the quantitative relationship between MMM signal and damage characteristic has not been fully revealed. In this paper, analysis of influences of stress and discontinuous structure on MMM signals is presented. Samples of diverse types of structural defects were made and then loaded with different series of stress. The resulting residual deformations and gradients of the magnetic field were measured. Analysis of the quantitative relationship between MMM signals and stress was conducted. The results show: 1) The magnetic field gradient above the structure-continuous zone increases with stress. Therein lies the potential to estimate the stress level point by point. 2) The second-order gradient of the magnetic field near the structure-discontinuous zone increases with the maximum strain in a similar way for different types of discontinuous structures, implying a possibility that the second-order gradient of the magnetic field may be conceive some information about the maximum stress at the edge of discontinuous structures independent of their geometry characteristics.
Fast Characterization of Multiple Cracks in Conductive Media Based on Adaptive Feature Extraction and SVR
Authors: Ahmed Shamim  , Miorelli Roberto  , Reboud Christophe  , Calmon Pierre  , Anselmi Nicola  , Salucci Marco
Published: February 2025

This paper describes real-time cracks characterization and localization inside a Structure Under Test (SUT) by exploiting Learning by Examples (LBE) strategy in the context of Eddy Current Testing (ECT). Within the framework of LBE, an optimal training set has been generated in offline phase by adopting Partial Least Squares (PLS) feature extraction combined with a customized version of output space filling (OSF). Support Vector Regression (SVR) algorithm is utilized for developing an accurate model based on the training set and subsequently real-time inversion (online phase) has been performed on unknown test set. The robustness of the proposed PLS-OSF/SVR approach is numerically assessed in presence of synthetic noisy test set.
Thickness Measurements Using Extremely Low Frequency Eddy Current Testing via TMR Sensors Operated with AC Modulation
Authors: Majima Yatsuse  , Nakamura Yoshihiro  , Sakai Kenji  , Kiwa Toshihiko  , Tsukada Keiji
Published: February 2025

Conventionally, eddy current testing (ECT) has been used for the surface crack detection. However, the detection of underside and internal corrosion was difficult using this technique. Therefore, we developed an underside and internal corrosion detection technique based on extremely low frequency ECT (ELECT). To use the TMR sensor for ELECT, a TMR sensor operated with AC modulation was applied as a magnetic sensor. Then, we developed the ELECT system using a TMR sensor operated with AC modulation, and succeeded in measuring the thickness of thick steel plate.
Fast Simulation Tools Dedicated to Pulsed Eddy Current Applications
Authors: Miorelli Roberto  , Reboud Christophe
Published: February 2025

This paper presents an efficient strategy to simulate pulsed eddy current testing signals. It relies on adaptive sparse grids interpolation for the fast evaluation of Fourier harmonics composing the transient pulsed eddy current signal. In addition to the discussion on the performance obtained, some useful post-processing procedures enabling to retrieve relevant information from raw signals are described.
Evaluation of a Dielectric Inclusion Using Inductive RF Antennas and Artificial Neural Networks for Tissue Diagnosis
Authors: Dinh Thi Hong Nhung  , Wang Mengze  , Serfaty Stéphane  , Placko Dominique  , Joubert Pierre-Yves
Published: February 2025

In this paper, the relevance of the non contact RF evaluation of the complex permittivity of organic material is addressed by means of a computational approach. The authors consider a simple configuration constituted of a single loop RF antenna interacting with a dielectric material mimicking a typical organic tissue (the electrical conductivity is 0.6 S/m and the dielectric constant is 80) which includes a buried inclusion (e.g. a tumor featuring a conductivity of 0.2 to 1.6 S/m and a dielectric constant ranging from 20 to 160). First a three dimensional semi analytical model (DPSM) is implemented in order to evaluate the sensitivity of such an antenna to the complex permittivity of the buried inclusion. Then, the inverse problem which consists in evaluating the complex permittivity, the size and the location of the inclusion is addressed by means of an artificial neural network (ANN) approach. For the considered configuration (5 mm radius antenna, 40 mm radius spherical inclusion buried at a 5 to 20 mm depth within the tissue, antenna operated at 135 MHz), the main conclusions are that the complex permittivity and the depth of the inclusion can be fairly estimated (estimation error smaller than 5%), even in the case of antenna positioning uncertainties, providing the ANN is adequately trained. Also, a double antenna configuration significantly enhances the estimation of the location and size of the inclusion.
Measurement of Plating Thickness with High Liftoff Using Eddy Current Testing
Authors: Moriyasu Keisuke  , Kosaka Daigo  , Kakishita Kazuhiko  , Hashimoto Mitsuo  , Koyama Fumio
Published: February 2025

This study deals with measurement of the thickness of a zinc layer deposited on steel in the process of zinc plating. A short distance between the blower that blows liquid zinc and the sensor that measures the thickness of the layer, and a long distance between the sensor and test samples are required in the process. We attempted to apply eddy current testing to measure the thickness of the zinc layer with high liftoff. This paper demonstrates the eddy current distribution calculated using the finite element method and the measured liftoff and thickness dependencies at each excitation frequency of an eddy current sensor. The frequency that results in a phase difference of approximately 90° between dependencies was determined. This indicates that the thickness and liftoff length signals can be separated from the measured signal when an appropriate excitation frequency is selected. These results demonstrate the possibility of making thickness measurements using an eddy current with high liftoff.
Influence of the Plastic Deformation on Magnetic Properties of Austenitic Steels
Authors: Smetana Milan  , Chudacik Vladimir  , Capova Klara  , Palcek Peter  , Behun Lukas
Published: February 2025

Austenitic stainless biomaterials are worldwide used in biomedical practice. Electromagnetic nondestructive evaluation allows their contactless investigation in terms of magnetic field mapping. Intrinsic magnetic field directly reflects changes in their mechanical properties. This article presents investigation of concrete austenitic biomaterials by use of fluxgate sensor. Pre-defined plastic deformation levels of the cylindrical specimens are evaluated. Three-dimensional scanning procedure has been performed.
Fabrication of Imitative Stress-Corrosion Cracks for Electromagnetic NDE Using Synchrotron Deep X-Ray Lithography and Powder Metallurgy Processing
Authors: Jomdecha Cherdppong  , Cai Wenlu  , Xie Shejuan  , Chen Zhenmao  , Phung-On Isaratat  , Pattanakul Rungrueang
Published: February 2025

This paper presents advanced methods using the deep X-ray lithography (X-ray LIGA) and the powder metallurgy processing to fabricate imitative Stress Corrosion Crack (SCC) for electromagnetic NDE as needed to be a reference specimen. A pattern of the imitative SCC was formed by using X-ray LIGA with controllable shape and size with precise details of SU-8 material. The prepared pattern was then inserted into mixed-stainless steel and Sn powders in a soft mold by using less pressure filling method. Afterward, the specimens were sintered at 1100°C. The results showed that these promising combined techniques can be utilized to fabricate micro sizes of the imitative SCCs. In addition, an inversion scheme was utilized to evaluate the equivalent conductivity of the specimens. The analysis results revealed that they were appropriated for desired equivalent conductivities of actual SCC detection by an eddy current testing.
High-Speed MFL Method Based on Multistage Magnetization Structure
Authors: Yang Li-jian  , Geng Hao  , Gao Song-wei
Published: February 2025

In the process of magnetic flux leakage (MFL) testing, the leakage magnetic field decreases as the testing velocity increases, which leads to the severe detection omission. To address this issue, factors affecting high-speed magnetic flux leakage testing are analyzed, multistage magnetization structure is then proposed. By comparing magnetic flux leakage signals characteristics on the surface of steel pipe with high speed and low speed, effectiveness and feasibility of multistage magnetization structure are analyzed. Results show that multistage magnetization structure can effectively extend magnetization time. Compared with single magnetization, the magnetic flux leakage signal of the inner wall of the steel pipe can be detected effectively with multistage magnetization structure at high speed, and the magnetic flux leakage testing precision is improved.
Parallel Ray Tracing Algorithm for Numerical Analysis in Radiative Media Physics
Authors: Olkhovskaya Olga  , Kotelnikov Alexey  , Yakobovskiy Mikhail  , Gasilov Vladimir
Published: February 2025

An original algorithm was developed for ray tracing across unstructured 3D grid. The resulting set of rays provides the base for the grid-characteristic computation of radiative energy transfer in the high temperature plasma simulations using MPI parallel technique and grid decomposition. The algorithm is implemented as a C++ code and incorporated in 3D magneto hydrodynamic (MHD) Eulerian code. Accurate rational calculations using integers of unlimited range are applied for the intersection of rays with grid elements. Tracing of different rays within a single MPI process is carried out in parallel with the use of OpenMP threads. Acceleration and scalability of the implemented algorithms were investigated including a comparison with other solvers within MHD code. The developed algorithm enables accounting the anisotropy of the radiation field in complex multiscale 3D MHD simulations. Proposed applications are the following: 1) postprocessing the results of 3D simulations (numerical pinhole imaging, estimation of the radiation output in a given direction); 2) modeling the propagation of the laser radiation; 3) calculating the thermal radiation field on the basis of quasi-diffusion model (improvement of the Eddington factor for the diffusion approximation).
Application of Eisenstat-SSOR Preconditioner to Realistic Stress Analysis Problems by Parallel Cache-Cache Computing
Authors: Abe Kuniyoshi  , Fujino Seiji
Published: February 2025

We are to solve a linear system of equations which arise from discretization of realistic stress analysis problems by Krylov subspace (KS) methods such as the Conjugate Gradient (CG) method and the AZMJ variant of ORTHOMIN(2). It is important to use effective preconditioners with the KS methods to rapidly and successfully solve the linear equations. However some of the preconditioners do not often work well, and the speed-up obtained by parallel computing deteriorates when using preconditioning. We therefore try to apply CG and AZMJ with an Eisenstat type of Symmetric SOR preconditioner (abbreviated as E-SSOR) for parallel computing to the realistic stress analysis problems, and then examine the effectiveness of E-SSOR and the parallel performance. The numerical experiments demonstrate that AZMJ and CG using E-SSOR for parallel computing are useful for obtaining rapid and successful convergence on the stress analysis problems, and the good speed-up can be gained by E-SSOR on parallel computer. The convergence behavior of AZMJ is superior to that of CG, and the parallel performance of AZMJ, which has the less number of synchronization points than CG, using the hybrid parallelization is higher on the parallel computer.
CalCul: A Python-Based Workspace for High-Performance Parameters-Sweep in Scientific Legacy Codes
Authors: Oren Gal  , Malamud Guy
Published: February 2025

Scientific legacy codes are usually holding large numbers of physical arrays which have been used and updated by the code routines, and the code parameters are set using a simple textual data file. Therefore, in cases when there is a need to perform a parallel large-scale parameters-sweep, the user needs to manually replicate the code multiple times, change the data file to its needs, run the code and control its performances - at each working directory separately. This task is practicable when there is a need to create several computations, but impossible when there is a need to run a large-scale parallel parameters-sweep on a cluster with thousands of calculations. In this work we present CalCul – A Python-based Workspace for High-Performance Legacy Scientific Codes, which is able to automatically render most of legacy codes data files into a python object, allowing the user – using many other functionalities – to control and handle all the needs of a parameters-sweep using the simplicity and sophistication of Python libraries, and thus allowing the conversion of work with software from manual to automatic. Also, in order to not damage the way legacy software work, and in order to abstain from replacing existing work modules for these software, the CalCul system provides a mirror between the changes done using the Python object and the legacy data files, and thus allows working dually on the software – either from CalCul or from the data files itself. CalCul also interfaces with IPython interactive command shell, and in this way, the user can manage all of his scientific actions entirely in one hermetic workspace.
Comparing Actor System Topologies and Parameters Using BeCoMe
Authors: Grebe Marco  , Lacko Tilman  , Loogen Rita
Published: February 2025

Benchmarking and rating constantly running, distributed, message based systems is very difficult. Time based benchmarks require high effort and are very error prone and expensive because they need dedicated hardware. Instead we propose to use the number of messages in such systems as an abstract layer which is independent from physical clusters but nevertheless allows to infer physical behaviour using hardware specific cost vectors. The number of messages and cost vectors can be systematically extracted from a running instance of a message based system. We show how results gained using a traditional benchmark and those inferred using our technique correlate using two real world examples.
A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering
Authors: Spezzano Giandomenico  , Vinci Andrea
Published: February 2025

In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures.