Hasil untuk "Cellular telephone services industry. Wireless telephone industry"

Boyi Liu, Yongguang Lu, Jianguo Zhao et al.

Large Language Model (LLM) services fundamentally differ from traditional Deep Neural Network (DNN) applications in wireless networks. We identify three critical distinctions: (1) unlike traditional DNNs with unidirectional data flows, LLM's multimodal interactions create bidirectional heavy loads with contrasting bottlenecks, requiring direction-aware resource scheduling; (2) while traditional DNNs exhibit fixed computational patterns, LLM's highly variable inference times interact complexly with network slicing, causing dynamic bottleneck migration; and (3) in contrast to predictable DNN traffic, LLM's token streams demonstrate unprecedented burstiness and state dependencies. These insights motivate WiLLM, the first open-source framework, implemented as a wireless platform, for LLM service research. Built on OpenAirInterface, WiLLM introduces several technical innovations: dynamic slice compatibility, universal UE compatibility through application-layer tunneling, multi-UE multi-slice scheduling, dual-mode resource allocation, and cross-layer APIs. In addition, WiLLM eliminates the need for specialized wireless expertise, enabling researchers and developers to experiment with LLM services over realistic cellular networks. We demonstrate the platform's capabilities through a smart glasses case study and provide a comprehensive dataset of \~1.6 million synchronized measurements. The complete system, dataset, and appendix are available at https://openwillm.github.io.

Aydin Homay

Design is a fundamental aspect of engineering, enabling the creation of products, systems, and organizations to meet societal and/or business needs. However, the absence of a scientific foundation in design often results in subjective decision-making, reducing both efficiency and innovation. This challenge is particularly evident in the software industry and, by extension, in the domain of industrial control and automation systems (iCAS). In this study, first we review the existing design definitions within the software industry, challenge prevailing misconceptions about design, review design definition in the field of design theory and address key questions such as: When does design begin? How can design be defined scientifically? What constitutes good design? and the difference between design and design language by relying on advancements in the field of design theory. We also evaluate the distinction between ad-hoc and systematic design approaches, and present arguments on how to balance complementary operational concerns while resolving conflicting evolutionary concerns.

Todd A. Brun, Leonard Mlodinow

We show that free QED is equivalent to the continuous-space-and-time limit of Fermi and Bose lattice quantum cellular automata theories derived from quantum random walks satisfying simple symmetry and unitarity conditions. In doing so we define the Fermi and Bose theories in a unified manner using the usual fermion internal space but a boson internal space that is six-dimensional. We show that the reduction to a two-dimensional boson internal space (two helicity states arising from spin-1 plus the photon transversality condition) comes from restricting the quantum cellular automaton theory to positive energies. We briefly examine common symmetries of quantum cellular automata, and how time-reversal symmetry demands the existence of negative-energy solutions. These solutions produce a tension in coupling the Fermi and Bose theories, in which the strong locality of quantum cellular automata seems to require a nonzero amplitude to produce negative-energy states, leading to an unphysical cascade of negative-energy particles. However, we show in a 1D model that by extending interactions over a larger (but finite) range it is possible to exponentially suppress the production of negative-energy particles to the point where they can be neglected.

John Oyekan, Christopher Turner, Michael Bax et al.

The rapid advancement of Large Language Models (LLMs) has resulted in interest in their potential applications within manufacturing systems, particularly in the context of Industry 5.0. However, determining when to implement LLMs versus other Natural Language Processing (NLP) techniques, ontologies or knowledge graphs, remains an open question. This paper offers decision-making guidance for selecting the most suitable technique in various industrial contexts, emphasizing human-robot collaboration and resilience in manufacturing. We examine the origins and unique strengths of LLMs, ontologies, and knowledge graphs, assessing their effectiveness across different industrial scenarios based on the number of domains or disciplines required to bring a product from design to manufacture. Through this comparative framework, we explore specific use cases where LLMs could enhance robotics for human-robot collaboration, while underscoring the continued relevance of ontologies and knowledge graphs in low-dependency or resource-constrained sectors. Additionally, we address the practical challenges of deploying these technologies, such as computational cost and interpretability, providing a roadmap for manufacturers to navigate the evolving landscape of Language based AI tools in Industry 5.0. Our findings offer a foundation for informed decision-making, helping industry professionals optimize the use of Language Based models for sustainable, resilient, and human-centric manufacturing. We also propose a Large Knowledge Language Model architecture that offers the potential for transparency and configuration based on complexity of task and computing resources available.

Guanhongye Peng, Xincheng Ren, Lixin Guo et al.

This research involved studying the characteristics of composite electromagnetic scattering from a rocket moving above actual terrain using the finite-difference time-domain method. The angular distribution curve of the composite scattering coefficient was obtained, and the influences of factors such as the angle of incidence, the frequency of the incident electromagnetic wave, the content of moisture in soil, the dielectric constant of the rocket-shell material, the height of the rocket, and the undulation of the terrain on the composite scattering coefficient were investigated. Results show that the composite scattering coefficient oscillates with the scattering angle and increases in the direction of mirror reflection. It also decreases with increasing angle of incidence, frequency of the incident wave, and altitude of the rocket, while it increases with increasing soil moisture and dielectric constant of the rocket-shell materials. Although the influence of different terrain undulations on the composite scattering coefficient is noticeable, it follows no fixed pattern.

Yifei Yuan, Yuhong Huang, Xin Su et al.

Reconfigurable intelligent surface (RIS) is an emerging technology for wireless communications. In this paper, extensive system level simulations are conducted for analyzing the performance of multi-RIS and multi-base-station (BS) scenarios, by considering typical settings for industry standards. Pathloss and large-scale fading are taken into account when modeling the RIS cascaded and direct links. The performance metrics considered are the downlink reference signal received power (RSRP) and the signal to interference noise ratio (SINR). The evaluation methodology is compatible with that utilized for technology studies in industry standards development organizations, by considering the uniqueness of RIS. The simulations are comprehensive, and they take into account different layouts of RIS panels and mobiles in a cell, and different densities and sizes of RIS panels. Several practical aspects are considered, including the interference between RIS panels, the phase quantization of RIS elements, and the failure of RIS elements. The impact of near field effects for the RIS-mobile links is analyzed as well. Simulation results demonstrate the potential of RIS-aided deployments in improving the system capacity and cell coverage in 6G mobile systems.

Hambur Wang

This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.

Prashnna Ghimire, Kyungki Kim, Manoj Acharya

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLM) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI's early-stage adoption within the construction sector. Given GenAI's unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors' opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture & engineering domains.

Mauro Parise

An accurate series-form explicit expression is derived for the voltage induced in the small receiving loop of a central loop electromagnetic sounding system positioned above a homogeneous earth model. The solution is obtained by converting the semi-infinite integral representation for the vertical magnetic field induced at the center of the two loops into a series of simpler integrals, amenable to analytical evaluation. The developed formulation is based on replacing the exponential term in the integrand of the field integral with its power series expansion with respect to the difference of the squares of the wavenumbers in air and in the conducting medium. As a result, the vertical magnetic field and the induced voltage are finally expressed as sums of the spherical Hankel functions depending on geometrical parameters and the wavenumbers. The derived solution exhibits advantages in terms of both time cost and accuracy, when compared to the conventional numerical schemes for evaluating the Sommerfeld-type integrals and to the previously published quasistatic solution to the considered problem.

Qinghua Liu, Yuanxin He, Kai Ding et al.

Direction of arrival (DOA) estimation has recently been developed based on sparse signal reconstruction (SSR). Sparse Bayesian learning (SBL) is a typical method of SSR. In SBL, the two-layer hierarchical model in Gaussian scale mixtures (GSMs) has been used to model sparsity-inducing priors. However, this model is mainly applied to real-valued signal models. In order to apply SBL to complex-valued signal models, a general class of sparsity-inducing priors is proposed for complex-valued signal models by complex Gaussian scale mixtures (CGSMs), and the special cases correspond to complex versions of several classical priors are provided, which is helpful to analyze the connections with different modeling methods. In addition, the expression of the SBL form of the real- and complex-valued model is unified by parameter values, which makes it possible to generalize and improve the properties of the SBL methods. Finally, the SBL complex-valued form is applied to the offgrid DOA estimation complex-valued model, and the performance between different sparsity-inducing priors is compared. Theoretical analysis and simulation results show that the proposed algorithm can effectively process complex-valued signal models and has lower algorithm complexity.

Mark Yampolskiy, Paul Bates, Mohsen Seifi et al.

Security of Additive Manufacturing (AM) gets increased attention due to the growing proliferation and adoption of AM in a variety of applications and business models. However, there is a significant disconnect between AM community focused on manufacturing and AM Security community focused on securing this highly computerized manufacturing technology. To bridge this gap, we surveyed the America Makes AM community, asking in total eleven AM security-related questions aiming to discover the existing concerns, posture, and expectations. The first set of questions aimed to discover how many of these organizations use AM, outsource AM, or provide AM as a service. Then we asked about biggest security concerns as well as about assessment of who the potential adversaries might be and their motivation for attack. We then proceeded with questions on any experienced security incidents, if any security risk assessment was conducted, and if the participants' organizations were partnering with external experts to secure AM. Lastly, we asked whether security measures are implemented at all and, if yes, whether they fall under the general cyber-security category. Out of 69 participants affiliated with commercial industry, agencies, and academia, 53 have completed the entire survey. This paper presents the results of this survey, as well as provides our assessment of the AM Security posture. The answers are a mixture of what we could label as expected, "shocking but not surprising," and completely unexpected. Assuming that the provided answers are somewhat representative to the current state of the AM industry, we conclude that the industry is not ready to prevent or detect AM-specific attacks that have been demonstrated in the research literature.

Chaoqun Jiao, Yang Xu, Xiang Li et al.

Aiming at eliminating the leakage of magnetic fields from the wireless power transfer (WPT) system, the structural and working characteristics of the WPT system for the inspection robot are analyzed and an electromagnetic shielding method combining passive shielding and active shielding is proposed in this paper. Firstly, we simulated the magnetic field distribution of the WPT system in Maxwell. Secondly, passive shielding is configured in the WPT system, and the material, size, and position of the passive shielding are studied. Then, we add active shielding to areas where passive shielding cannot achieve a good shielding effect. Based on the analysis and summary of the two methods, we shield the WPT system in the horizontal direction with the appropriate size and distance of aluminum plate, and in the vertical direction, we use the active shielding coils. Simulation and experimental results show that the scheme only slightly reduces the transmission efficiency of the system (from 80.2% to 77.6%), but the shielding ability is 34.06% higher than that of only aluminum plates. The excellent effect of the proposed shielding method is verified in our experiment.

Ziaur Rahman, Ibrahim Khalil, Xun Yi et al.

There has been an intense concern for security alternatives because of the recent rise of cyber attacks, mainly targeting critical systems such as industry, medical, or energy ecosystem. Though the latest industry infrastructures largely depend on AI-driven maintenance, the prediction based on corrupted data undoubtedly results in loss of life and capital. Admittedly, an inadequate data-protection mechanism can readily challenge the security and reliability of the network. The shortcomings of the conventional cloud or trusted certificate-driven techniques have motivated us to exhibit a unique Blockchain-based framework for a secure and efficient industry 4.0 system. The demonstrated framework obviates the long-established certificate authority after enhancing the consortium Blockchain that reduces the data processing delay, and increases cost-effective throughput. Nonetheless, the distributed industry 4.0 security model entails cooperative trust than depending on a single party, which in essence indulges the costs and threat of the single point of failure. Therefore, multi-signature technique of the proposed framework accomplishes the multi-party authentication, which confirms its applicability for the real-time and collaborative cyber-physical system.

Karl Löwenmark, Cees Taal, Stephan Schnabel et al.

In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digitised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportunity to develop technical language supervision (TLS) solutions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on SentenceBERT and contrastive learning based zero-shot inference on annotated industry data.

S. Dasanayaka, Omar Al Serhan, Mina Glambosky et al.

This study aims to identify and analyze factors affecting the business-to-business (B2B) relationship between Sri Lankan telecommunication operators and vendors. The authors conduct a survey and develop models to explain relationship strength and satisfaction. The authors find that telecommunication operators and vendors value trust, commitment, adaptation and communication. Operator satisfaction varies by perception of product quality, service support, delivery performance, supplier know-how and value for money. The vendor’s relationship strength is impacted by trust and commitment; vendor satisfaction is affected by economic factors and referencing. The authors suggest formulating management strategies using these results to strengthen business relationships.,The authors develop two conceptual models to analyze the supplier and customer perspectives. This study’s drafted models were drawn from established models and were presented to experts in the industry, both telecommunication operators and vendors. Models were modified based on experts’ feedback, and hypotheses were developed from the conceptual models, developed separately for the two perspectives. Data collection was done via questionnaires; 150 questionnaires were sent via email to identified telecommunication operators and 100 questionnaires were sent via email to identified telecommunication vendors, with follow-up emails and telephone calls to improve response rates.,This study’s findings show that employees in the telecommunication industry recognize the importance of B2B relationships. Employees of both telecommunication operators and vendors agree that stronger relationships are advantageous. The correlation and regression analysis results identify factors that affect the B2B relationship. The following factors impact the strength of B2B relationships irrespective of view point: trust, commitment and satisfaction. The following factors were found to significantly affect the strength of B2B relationships between telecommunication operators and vendors from the operator perspective: adaptation and communication.,To enhance relationship strength, the management of operator organizations should take action to improve trust, commitment and satisfaction. Demonstrating honesty and integrity when dealing with vendors and exhibiting concern for the other party’s interests can help establish trust or enhance trust in existing relationships. Displaying commitment toward the vendor will also facilitate stronger relationships. Reasonable profits for both parties and sizeable business volume will also help satisfy vendors, increasing relationship strength. Positive referencing of the vendor in industrial and public forums will improve vendor satisfaction, enhancing relationship strength. Reputational capital can be built and maintained for both operators and vendors by keeping promises and defending the other party to outsiders. For managers of telecommunications operators and vendors in other emerging markets, this study’s results are important and can inform internal business practices to support trust, commitment and satisfaction.,This study contributes to the existing literature in two ways, a focus on the telecommunication industry and a previously unexplored emerging market, Sri Lanka. In addition, this study includes an analysis of the relationship from both the operator and vendor perspectives.

Fu Qiao, Yan Yan

Using the carefully selected industry classification standard, we divide 102 industry securities indices in China's stock market into four demand-oriented sector groups and identify demand-oriented industry-specific volatility spillover networks. The "deman-oriented" is a new idea of reconstructing the structure of the networks considering the relationship between industry sectors and the economic demand their outputs meeting. Networks with the new structure help us improve the understanding of the economic demand change, especially when the macroeconomic is dramatically influenced by exogenous shocks like the outbreak of COVID-19. At the beginning of the outbreak of COVID-19, in China's stock market, spillover effects from industry indices of sectors meeting the investment demand to those meeting the consumption demands rose significantly. However, these spillover effects fell after the outbreak containment in China appeared to be effective. Besides, some services sectors including utility, transportation and information services have played increasingly important roles in the networks of industry-specific volatility spillovers as of the COVID-19 out broke. By implication, firstly, being led by Chinese government, the COVID-19 is successfully contained and the work resumption is organized with a high efficiency in China. The risk of the investment demand therefore was controlled and eliminated relatively fast. Secondly, the intensive using of non-pharmaceutical interventions (NPIs) led to supply restriction in services in China. It will still be a potential threat for the Chinese economic recovery in the next stage.

Razin Farhan Hussain, Alireza Pakravan, Mohsen Amini Salehi

Cloud-based Deep Neural Network (DNN) applications that make latency-sensitive inference are becoming an indispensable part of Industry 4.0. Due to the multi-tenancy and resource heterogeneity, both inherent to the cloud computing environments, the inference time of DNN-based applications are stochastic. Such stochasticity, if not captured, can potentially lead to low Quality of Service (QoS) or even a disaster in critical sectors, such as Oil and Gas industry. To make Industry 4.0 robust, solution architects and researchers need to understand the behavior of DNN-based applications and capture the stochasticity exists in their inference times. Accordingly, in this study, we provide a descriptive analysis of the inference time from two perspectives. First, we perform an application-centric analysis and statistically model the execution time of four categorically different DNN applications on both Amazon and Chameleon clouds. Second, we take a resource-centric approach and analyze a rate-based metric in form of Million Instruction Per Second (MIPS) for heterogeneous machines in the cloud. This non-parametric modeling, achieved via Jackknife and Bootstrap re-sampling methods, provides the confidence interval of MIPS for heterogeneous cloud machines. The findings of this research can be helpful for researchers and cloud solution architects to develop solutions that are robust against the stochastic nature of the inference time of DNN applications in the cloud and can offer a higher QoS to their users and avoid unintended outcomes.

Nicola Melluso, Silvia Fareri, Gualtiero Fantoni et al.

Scientific discoveries and technologies played a significant role in the digital revolution that occurred over the last years. But what is their role in the turmoil brought by the current pandemic? The aim of this paper is to show how digital technologies are operating during this first phase of the spreading of COVID-19. The study analyses and debates the current and potential role of digital technologies, focusing on their influence in the industrial and social fields. More specifically we used the blogging platform "Medium", which has seen an exponential growth in its production of articles over the last couple of months. Even if different from esteemed scientific sources, this platform provides a structure that suits our analysis. We searched how many times digital technologies are mentioned in articles regarding Coronavirus and, after collecting these articles, we collected page tags (comparable to "keywords" in scientific articles) and classified them (technology tags and non-technology tags), to create a graph showing the relation between them. This network allowed us to acknowledge and picture how technologies are currently debated. This was the starting point to discuss the key implications for an imminent future, and question about the impact on industry, society and labour market. What are the opportunities or threats of using technologies of Industry 4.0? Which are the needs rising because of the pandemic and how can technologies help in their fulfillment? How will the industrial scenario change after this pandemic? How will the labour market be affected? How can technologies be advantageous in the emerging social challenges?

Weiwen Li, Yongcong Liu, Jie Li et al.

The characteristic mode theory (CMT) can provide physically intuitive guidance for the analysis and design of antenna structures. In CMT applications, the antenna current distribution is decomposed into the superposition of multiple characteristic modes, and the proportion of each current mode is characterized by the modal weighting coefficient (MWC). However, different characteristic currents themselves have different radiation efficiencies reflected by the eigenvalues. Therefore, from the perspective of the contribution to the radiation field, the modal proportion should be more accurately determined by the combination of the modal weighting coefficient and the mode current itself. Since the discrete mode currents calculated using the electromagnetic numerical method are distributed on the whole conductor surface, we can actually use the radiation field to quantify the modal proportion or estimate it using the far field in the maximum radiation direction. The numerical examples provided in the paper demonstrate that this modal proportion can effectively evaluate antenna performance.

Mingzhe Chen, Zhaohui Yang, Walid Saad et al.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.