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Point clouds are utilized to enable automated engineering applications for their ability to represent spatial geometry. However, they inherently lack detailed surface textures, posing challenges in differentiating objects at the texture level. Hence, this study introduces a 2D–3D fusing approach, leveraging material properties recognized from registered images as an augmented feature to enhance deep learning methods for the segmentation of building elements within point clouds. The proposed method was evaluated quantitatively on a 3D indoor data set with an implementation in an office room. The results are promising, showing improvement in recognition performance, particularly for objects with similar geometry but having different material properties. For instance, the segmentation of boards increased by 70.87%, and doors improved by 41.06% using the PointNet architecture. This enhanced segmentation not only reduces the time for interpreting point clouds but also has the potential to benefit downstream applications such as Scan‐to‐building information modeling (BIM), as defining regions for objects is essential. [ABSTRACT FROM AUTHOR]

With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast amount of available data poses a significant challenge. Traditional methods often struggle to capture the nuanced and hierarchical nature of user interests within the tourism domain. This paper pioneers the integration of domain information modeling technology into the realm of online review information mining, presenting a novel approach to constructing a user tourism interest model. Unlike existing methods, which primarily rely on flat or simplistic representations of user data, our approach leverages the hierarchical structure inherent in tourism domain information modeling. By harnessing big data within the tourism domain, we construct hierarchical tourism attributes and apply a conditional random field model along with an affective dictionary to facilitate the hierarchical mining of user travel interest information. This culminates in the establishment of a comprehensive user travel interest model using advanced information modeling techniques. Building upon this foundation, we further propose a dynamic user travel interest model, showcasing its adaptability and responsiveness to changing user preferences. Finally, we validate the accuracy and effectiveness of our model through simulation experiments within a user travel recommendation system, demonstrating significant improvements over traditional methods. [ABSTRACT FROM AUTHOR]

Aiming at the problems of data integration and information sharing in the calibration workshop of smoke alarm, combining OPC Unified Architecture (OPC UA) and digital twin technology, the OPC UA based digital twin smoke alarm calibration workshop information network framework is proposed. Using OPC UA information modeling technology, a digital workshop information model for smoke alarm calibration was constructed, and the static attribute set, process attribute set, and functional component set of the workshop were defined in detail. Taking the smoke box as an example, the information model construction was instantiated, and data collection and transmission were carried out with the digital twin model of the smoke box, achieving the implementation mapping between the twin model and physical entities. The results show that based on the proposed OPC UA information model, the interconnection and intercommunication of data at all levels of the digital twin smoke alarm calibration workshop can be achieved, and the workshop can be visualized, digitized, and flexible. [ABSTRACT FROM AUTHOR]

Unauthenticated device access to a network presents substantial security risks. To address the challenges of access and identification for a vast number of devices with diverse functions in the era of the Internet of things (IoT), we propose an IoT device identification method based on hardware and software fingerprint features. This approach aims to achieve comprehensive "hardware–software–user" authentication. First, by extracting multimodal hardware fingerprint elements, we achieve identity authentication at the device hardware level. The time-domain and frequency-domain features of the device's transient signals are extracted and further learned by a feature learning network to generate device-related time-domain and frequency-domain feature representations. These feature representations are fused using a splicing operation, and the fused features are input into the classifier to identify the device's hardware attribute information. Next, based on the interaction traffic, behavioral information modeling and sequence information modeling are performed to extract the behavioral fingerprint elements of the device, achieving authentication at the software level. Experimental results demonstrate that the method proposed in this paper exhibits a high detection efficacy, achieving 99% accuracy in both software and hardware level identification. [ABSTRACT FROM AUTHOR]

Defect inspection of existing buildings is receiving increasing attention for digitalization transfer in the construction industry. The development of drone technology and artificial intelligence has provided powerful tools for defect inspection of buildings. However, integrating defect inspection information detected from UAV images into semantically rich building information modeling (BIM) is still challenging work due to the low defect detection accuracy and the coordinate difference between UAV images and BIM models. In this paper, a deep learning-based method coupled with transfer learning is used to detect defects accurately; and a texture mapping-based defect parameter extraction method is proposed to achieve the mapping from the image U-V coordinate system to the BIM project coordinate system. The defects are projected onto the surface of the BIM model to enrich a surface defect-extended BIM (SDE-BIM). The proposed method was validated in a defect information modeling experiment involving the No. 36 teaching building of Nantong University. The results demonstrate that the methods are widely applicable to various building inspection tasks. [ABSTRACT FROM AUTHOR]

Configurators have recently emerged as essential tools in the construction industry to enable builders to offer a wide range of customizable designs. Due to significant challenges in information integration between construction suppliers and clients, existing configurator systems often lack crucial usability and supply chain information, presenting barriers to wider adoption among residential communities, especially in single-family residence development that requires a high degree of customization. To address this challenge in the design and construction supply chain, this study presents a lightweight cloud-based modular home configuration methodology as a robust unified platform solution to integrate parametric design options with a certified kit-of-parts library to meet local design codes. The configurator prototype developed under this framework seamlessly integrates essential design and supply chain information by leveraging (1) a generative layout design with pre-approved blueprints, (2) a knowledge-based recommender system to link the design process with certified material catalogs, and (3) a user-friendly web interface to present possible designs. The implementation of a single-family housing design adhering to the building codes in the British Columbia Province of Canada illustrates the benefits of the proposed configurator functionalities and efficient supplier data integration. Lightweight and automated, the proposed configurator has substantial potential to be scaled and adopted across different communities. [ABSTRACT FROM AUTHOR]

The construction sector of the Kingdom of Saudi Arabia (KSA), valued at USD 152 billion and employing 2.54 million people by 2023, is crucial to its economy. However, it faces challenges such as delays, disputes, and cost overruns. This study aims to address these issues by implementing Building Information Modeling (BIM) in the KSA, based on an extensive literature review highlighting the role and significance of BIM in mitigating construction claims. BIM improves collaboration, communication, and data integration among stakeholders. Hence, this study proposes a comprehensive BIM Package framework comprising Revit Architecture, Microsoft Project, and Cost-X to reduce claims effectively. Validated through a KSA claims case study with a USD 1,870,000 claims value and 360-day delay, the BIM Package significantly reduced the claims value to USD 188,000 and saved 275 days. Interview sessions were conducted to validate the effectiveness of the BIM Package; 95% favored the use of BIM, 86% supported NEC4 contract adoption alongside BIM, 100% acknowledged BIM's potential in reducing design errors, 95% were confident in the case study's accuracy, and 82% were satisfied with data accuracy. This study confirms that BIM is an effective approach for minimizing construction claims in the KSA. [ABSTRACT FROM AUTHOR]

District energy management systems (DEMS) offer the capability to harness the energy potential of districts. Currently, there is a lack of research describing the necessary underlying information systems. An overview of lessons learned is presented from a series of workshops with experts from industry, research, and further roles, predominately from Germany. Seven workshops discussing challenges within digitalization, data requirements, district energy system planning, common elements in district energy systems are conducted, ensuring long‐term operation and transferability, and validation of prior findings. Based on these discussions, insights into topics such as the key data and its requirements, typical distinctions in DEMS, their infrastructure, considerations in the information modeling, as well as challenges in data availability, are offered. These findings are mapped to a data value pipeline, illustrating key resources and tools needed to plan and operate DEMS. As demonstrated in this article, the requirements for DEMS highlight their complexity. Distinct categorizations, such as the ownership or heterogeneity in building and technologies, form the landscape of DEMS. The need for standardized, interoperable systems is often a contrast to the uniqueness of individual systems and their needs. Further adoption of the described concepts necessitates sustainable business models. [ABSTRACT FROM AUTHOR]

This paper describes virtual reality (VR) development using a 360-degree panoramic and Leaflet JavaScript (Leaflet JS) to introduce campus buildings in real-time. The campus building of Universitas Negeri Gorontalo (UNG) in Bone Bolango Regency was chosen as a case study. It allows users to navigate and listen to background sound and narration, open the site map interactively, and read brief information about each location. Each panorama contains hotspots that allow users to explore further. All images are combined using a photo-stitching technique to produce a panoramic image. The research method used is the multimedia development life cycle (MDLC), which consists of six stages: concept, design, material collection, assembly, testing, and distribution. Based on the system usability scale (SUS) test, the virtual tour reality website application received feedback from users regarding its usability, satisfaction, and effectiveness, and it is interesting to use this application. The results show that the website application can visualize the campus building environment with various layers of information and can create a very realistic and detailed representation of the campus environment. [ABSTRACT FROM AUTHOR]

The grid integration of large-scale photovoltaic and other distributed energy sources is an effective solution for addressing power supply shortages and environmental pollution. However, the widespread adoption of photovoltaics and grid integration presents various technological and management challenges. To fulfill the demands of grid management and ensure safe operations, the exchange of information between different terminals is continuously escalating. Use of diverse communication standards creates the problem of "information islands" among terminals. Hence, a standardized information model is crucial for describing the photovoltaic grid integration business and enhancing the efficiency of related software platform research and development. This study extends the IEC 61970/61968 standards and presents a common information model for the integration of photovoltaic systems into the grid. Initially, the operational procedures for integrating photovoltaic systems into the grid are analyzed, and UML modeling tools are employed for business modeling purposes. Subsequently, leveraging the outcomes of the business modeling and the content of the IEC 61970/61968 standards, the development of the common information model is executed. Lastly, causal analysis is conducted along with the modeling of communication standard extensions specifically targeted for the integration of photovoltaic systems into the grid, culminating in the finalization of the construction of the common information model for photovoltaic grid connection. [ABSTRACT FROM AUTHOR]

The city phenomenon is dynamic, complex, and has an abundance of data. City Information Modeling (CIM) and Digital Twins (DTs) are new data-driven approaches toward monitoring, controlling, and managing cities. The main purpose of this research is to introduce and raise the concept of CIM and DTs as a necessity in the smart city era for urban planning, design, and management and to implement a practical way to achieve these concepts in the Narmak neighborhood of Tehran City (Iran) as our case study. The primary tool is CityEngine software. A method was discovered to simulate cities where the existence and access to data are severely constrained. First, a Geographic Information System (GIS) database was assembled, then it was analyzed, and the relationships between urban data were determined. Then, programming in CityEngine generated a smart three-dimensional (3D) model with all the city's information. Finally, CIM and DTs led to the production of an integrated 3D database that is smart and interactive. [ABSTRACT FROM AUTHOR]

Developing a precise tree stem curve and robust estimation of stem volume are crucial for forest inventories with various applications. Laser scanned point clouds have been recognized as the most practical data for tree information modeling. Many methods for stem curve development involve estimating stem diameters at different heights and determining stem volume by utilizing fitted cylinders based on these diameters and the associated heights. The estimation of diameter depends on circle fitting. However, many circle fitting methods are non-robust and inaccurate in the presence of noise, outliers, and significant data gaps, resulting in faulty diameters and imprecise stem volume. Limited scanning, occlusions from the physical complexity, high tree density, and adjacent branches may cause data incompleteness, and generate outliers. To address these challenges, we employ robust statistical approaches to restrain the influence of outliers and data gaps. This paper contributes by (i) exploring the problems of robust diameter estimation for partial data, and in the presence of noise and outliers, (ii) understanding the impacts of using erroneous diameters in cylinder fitting, and later for stem curve and volume estimation, and (iii) developing a robust method that couples robust circle and cylinder fittings to derive precise stem curve and estimation of stem volume in the presence of outliers and partial data. We demonstrate the performance of the proposed algorithm through terrestrial laser scanning point clouds. [ABSTRACT FROM AUTHOR]

Object-based modeling has become an integral and more preferred design approach as against two-dimensional (2D) design representation. This is evident in the increased use of building information modeling (BIM) for building design and city information modeling (CIM) for city planning and development. However, the development and management of object-based city models are often siloed and do not coexist in a virtual environment. Urban infrastructure is often designed in isolation, yet it forms part of an integrated network of assets in real life. Blockchain is one of several technologies that has been integrated with BIM because of its transparent and tamper-proof features. This research proposes a framework for the lifecycle management of buildings and other urban assets by integrating blockchain technology (BCT) into a heterogenous CIM of multiowned assets using a nested, multilevel data environment. First, the BIM is synchronized into a GIS scene for geospecificity, then the BIM metadata is written onto a blockchain protocol distributing the model information across a network of project collaborators using a common data environment (CDE). The resultant CIM is then shared through an outer layered city level CDE. Use case scenarios are presented to validate the functionality of the proposed approach, and research limitations are discussed. The framework enables the critical interproject or interasset communication which is otherwise impossible in traditional approaches where individual assets are designed as stand-alone models. Practical Applications: Nowadays, nearly every physical product pre-exists in digital form for purposes of design, testing, and optimizing the product before its creation. This research argues that the city can also exist in digital form beyond a mere representation of its geometry, but encompassing the intangible information flow that exists between physical assets in real cities. This study proposes using City Information Modeling (CIM) that is enabled through blockchain technology (BCT) to record all activities concerning physical assets throughout their lifecycle. This is achieved by harnessing the various digital models that predate the physical components of the city from their design stages. These records of activities are retrievable and easily visualized interactively. Such CIM models are useful to trace the origins of building parts for accurate asset valuation or to review the maintenance services carried out on an asset and its impact on the constituent parts. It also provides a real template to commence new design projects whereby existing surrounding features, which are only possible through a site visit, can now be considered. The study takes into consideration the multiple ownership structure of city assets by integrating various digital models. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, the digital transformation of industrial systems necessitates advanced wireless communication solutions that are both agile and secure. Private 5G networks, characterized by their unparalleled bandwidth, reduced latency, and fortified security, have emerged as a beacon of innovation in this paradigm shift. This study explores the feasibility of deploying private 5G networks across diverse enterprises, underpinned by a robust methodological framework. Drawing from contemporary research, it is evident that while 5G promises transformative benefits for sectors like logistics, manufacturing, and autonomous vehicles, its adoption is riddled with challenges such as network oversaturation and frequency coordination. Analyzing 2006 companies, our findings reveal that only 103 enterprises align with the optimal criteria for private 5G network implementation, highlighting the intricate balance of spatial, economic, and regulatory considerations. This research not only offers a strategic roadmap for businesses and stakeholders but also positions private 5G networks as a pivotal tool for achieving operational excellence, enhanced data privacy, and seamless connectivity in the modern industrial landscape. [ABSTRACT FROM AUTHOR]

Since the open-pit precedence-constrained production scheduling problem is an NP-hard problem, solving it is always a challenging task, especially from a long-term perspective because a mineral deposit containing millions of blocks would require several million precedence arcs as constraints, making the solution time grow exponentially and making a direct approach unfeasible. Therefore, different strategies have been employed since the 1960s to reduce the size of this problem, such as determining the ultimate pit limit, subdividing it into phases, segmenting the production scheduling problem into long-, mid-, and short-term plans, as well as aggregating blocks into clusters, thus significantly reducing the number of precedence arcs. Different modeling and clustering strategies have already been employed in an attempt to reduce the size of the mine sequencing problem, such as layer modeling, re-blocking, bench-phase clustering, or polygon (mining cut) clustering based on a similarity function. The mining cut clustering problem has been solved lately by machine learning and heuristics techniques, and this approach can also introduce operational constraints to the mine sequencing problem, such as equipment size, minimum pit width, and preferential mining direction. In this study, we propose a mining cut clustering model based on Mixed Integer Linear Programming (MILP). Then we solve it by an exact approach and by Constraint Programming (CP), analyzing the strengths and weaknesses of the Constraint Optimization Problem (COP) and Constraint Satisfaction Problem (CSP) techniques. Numerical experiments were carried out on the Bench 15 of the Newman1 dataset, demonstrating the superiority of the COP approach. [ABSTRACT FROM AUTHOR]

The systematic review of Business Information Modeling (BIM) plays a crucial role in understanding its significance and impact. This review allows for a comprehensive examination of the existing literature, highlighting the benefits, challenges, and success factors associated with BIM. There is a scarcity of studies dealing with this subject, and so a question about the most important advantages that will be obtained by the construction industry, especially the construction companies, as a result of the BIM application arises. Relevant previous studies were reviewed and their quality was evaluated using a systematic methodology. The current study was characterized by the use of the SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis technique. As far as is known, the current study is the first of its kind in the field of Iraqi project management. The results suggest that the BIM benefits include firm's growth, organizational performance, enhanced market value, employee motivation, and service quality. [ABSTRACT FROM AUTHOR]

This study focuses on the integration of System Dynamics (SD), Artificial Intelligence (AI) technology, and 3D Urban Information Modeling (CityGML) in the field of urban planning. It aims to optimize urban layout to cope with rapid urban growth, emphasizing resource allocation through a combination of greedy strategies and AI methods. The core contribution of this paper is to provide spatial allocation of feedback from simulation techniques i.e. SD using the application of genetic algorithms (GA) based on a greedy strategy. The methodology was implemented using a fictitious case study in Berlin, where new students' dorms are required according to the growth in the number of students. The SD tool is used to simulate the growth over 5 years and determine the number of new dorms required. A genetic algorithm is used to optimize the planning objectives of allocating 16 new dorms in 186 possible locations. The algorithm effectively handles the feedback from SD and applies complex multi-objective optimization problems, addressing the challenge of accommodating a growing student population. It proposes strategic dormitory allocations that balance factors such as cost efficiency, campus accessibility, and proximity to public transportation systems. We use CityGML data to simulate and predict future urban transformation, providing dynamic and realistic representations for urban planning decisions and updating the original city models. [ABSTRACT FROM AUTHOR]

Although cities are increasingly permeated by information and communication technologies (ICTs), these urban centers still face theoretical and practical obstacles regarding the application of 3D technologies as a tool for urban planning and management. This study aims to ontologically structure an informational model as an urban management tool and assess the perceived challenges for implementing this model in the municipal public management of Curitiba, Brazil. The study was carried out through the collective subject discourse, the interviews' thematic saturation, and Delphi techniques. The results indicate that the technical capacity, technological infrastructure, and financial resources for the development of a city information modeling (CIM) exist, although its activation for such development depends on a more significant political and managerial entrepreneurship in the decision-making in arrangements of multilevel governance, and, consequently, there is a need to develop public policies for staff training to stimulate and promote advanced technological innovations for urban management and planning. Implementing a CIM in large cities seems to face more management and planning problems due to the lack of institutional ontologies than technological, infrastructural, or knowledge limitations. [ABSTRACT FROM AUTHOR]

The built environment is subject to various defects as it ages. A well-maintained built environment depends on surveying activities to inspect, document, and rehabilitate the defects that occurred. The advancement of digital technologies paves the pathway towards (1) comprehensive defect inspection by systematic mapping, (2) their consistent documentation by digital modeling, and (3) timely retrofitting by proactive management. However, the three steps of defect mapping, modeling, and management (D3M) remain largely fragmented and have yet to be synergized. Exploiting the pivotal role of building information modeling (BIM) in built asset management, this paper puts forward a cohesive framework for integrated D3M. It leverages the rich geometric-semantic information in BIM to assist defect mapping and enriches the BIM by industry foundation classes (IFCs)-represented defect information. The defect-enriched BIM facilitates defect management in a data-driven manner. The framework was applied in multiple real-life infrastructure and civil works projects. It demonstrates how the BIM-based D3M framework can enhance the maintenance of those that have been built, and ultimately contribute to a safe and sustainable built environment. Future studies are called for to substantiate each of the 3Ms by leveraging BIM as both an enabler and a beneficiary. [ABSTRACT FROM AUTHOR]

With the rising population and urbanization in developing countries, the building sector must contribute to sustainable construction. To achieve this, it must consider suitable design methodology, circular financial models, and holistic processes like information modeling, as collaboration is the fundamental tool for producing better outcomes. In the present study, different design alternatives, one conventional and the other with the proposed materials and methods were analyzed computationally for their cost and environmental impacts with the help of a residential building case. The results show that sustainable construction practices, such as low cost and less carbon-emitting materials reduced the overall cost by 25% and carbon emissions by 20%. Moreover, vital practices are recommended to meet the circular economy's objectives in building construction. [ABSTRACT FROM AUTHOR]

Climate change is a threat and crisis that hit the world today. The green industry is widely implemented in the manufacturing sector as an effort to reduce negative impacts on the environment. The implementation of the green industry is influenced by various factors. The Chemical Industry is one sector that faces challenges in implementing green industry practices. The objective of this paper is to create an innovative conceptual framework that combines blockchain technology and building information modeling. This research examines the concept of green retrofitting in the chemical industry using an assessment based on the Ministry of Public Works and Housing Regulation No. 21 of 2021. The study was conducted in a chemical industry located in Cilegon, Banten, Indonesia. The research method combines Blockchain-Building Information Modeling (BIM) to analyze the cost efficiency of green retrofitting and Structural Equation Modeling-Partial Least Squares (SEM-PLS) as a tool to process data from questionnaires and identify influential factors. The results indicate that the use of Blockchain-BIM can reduce retrofitting costs by 4.42% for low-level, 4.45% for medium-level, and 4.40% for high-level categories. This demonstrates that Blockchain-BIM has a significant impact on improving cost performance in the retrofitting process. [ABSTRACT FROM AUTHOR]

Semantic segmentation of high-resolution remote sensing urban images is widely used in many fields, such as environmental protection, urban management, and sustainable development. For many years, convolutional neural networks (CNNs) have been a prevalent method in the field, but the convolution operations are deficient in modeling global information due to their local nature. In recent years, the Transformer-based methods have demonstrated their advantages in many domains due to the powerful ability to model global information, such as semantic segmentation, instance segmentation, and object detection. Despite the above advantages, Transformer-based architectures tend to incur significant computational costs, limiting the model's real-time application potential. To address this problem, we propose a U-shaped network with Transformer as the decoder and CNN as the encoder to segment remote sensing urban scene images. For efficient segmentation, we design a window-based, multi-head, focused linear self-attention (WMFSA) mechanism and further propose the global–local information modeling module (GLIM), which can capture both global and local contexts through a dual-branch structure. Experimenting on four challenging datasets, we demonstrate that our model not only achieves a higher segmentation accuracy compared with other methods but also can obtain competitive speeds to enhance the model's real-time application potential. Specifically, the mIoU of our method is 68.2% and 52.8% on the UAVid and LoveDA datasets, respectively, while the speed is 114 FPS, with a 1024 × 1024 input on a single 3090 GPU. [ABSTRACT FROM AUTHOR]

Cognitive Reliability and Error Analysis Method (CREAM) is widely used in human reliability analysis (HRA). It defines nine common performance conditions (CPCs), which represent the factors that may affect human reliability and are used to modify the cognitive failure probability (CFP). However, the levels of CPCs are usually determined by domain experts, which may be subjective and uncertain. What's more, the classic CREAM assumes that the CPCs are independent, which is unrealistic. Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation. To address the issue of uncertain information modeling and processing, this paper introduces evidence theory to evaluate the CPC levels in specific scenarios. To address the issue of dependence modeling, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to process the dependence among CPCs and calculate the relative weights of each CPC, thus modifying the multiplier of the CPCs. The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable. [ABSTRACT FROM AUTHOR]

Multi-attribute group decision-making (MAGDM) is one of the research hotspots in human cognitive and decision-making theory. However, there are still challenges to the existing MAGDM methods in modeling uncertain linguistics of decision-makers' (DMs') cognitive information and objectively obtaining weights. Therefore, this paper aims to develop a new MAGDM method considering incomplete known weight information under spherical uncertain linguistic sets (SULSs) to model uncertain information in MAGDM problems. The method mainly includes the following aspects. Firstly, a new concept, which enables an intuitive evaluation of neutral membership and hesitancy degrees at the linguistic evaluation, has been is first developed for capturing the more uncertain information. Secondly, the cosine similarity measure (CSM) and cross-entropy measure (CEM) are widely used to measure ambiguous information because of their robustness of measurement results. The CSM and CEM are extended to SULSs to calculate the DMs' and attributes weights quantitively, respectively. Thirdly, in terms of effective integration of fuzzy information to obtain more accurate decision results, the Hamy mean (HM) and dual Hamy mean (DHM) operators are valued due to their consideration of the interrelationships between inputs. Two extension operators, named spherical fuzzy uncertain linguistic weight HM and DHM, are proposed to integrate spherical fuzzy uncertain linguistic information in the third stage. In the experiment, a decision case is presented to illustrate the applicability of the proposed method, and results show the effectiveness, flexibility and advantages of the proposed method are demonstrated by numerical examples and comparative analysis. [ABSTRACT FROM AUTHOR]

In prognostics and health management, the system's degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on information gathered by multiple sensors, which eventually causes data fusion-related complex problems. Typically, sensor information contains the speed, pressure, temperature, and similar other types of various system data. These systems' data obtained through sensors can be utilized as a part of the evidence in the evidence-based estimation method. In this work, an artificial intelligence-based novel framework for estimating the remaining useful life using data fusion has been presented. The Dempster–Shafer extended theory is adopted for sensor information modeling and data fusion. Besides, two different scenarios are introduced to determine the similarity between the studied system and the available evidence. As a case study, the turbofan dataset is demonstrated to assess the proposed method. Based on the results, our integrated proposed method performs very competitively compared with the existing methods based on standard scores and performance criteria. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, construction processes are shifting towards automation by implementing additive manufacturing (AM) of concrete structures, also referred to as concrete printing. Adapting concrete as a printing material entails complex material–process interactions between concrete and manufacturing processes that require specialized data modeling. However, data modeling for the AM of concrete structures has not kept up with concrete printing research and development. Aiming to enhance data modeling for the AM of concrete structures, this paper proposes a semantic modeling approach, referred to as "printing information modeling" (PIM). The PIM approach defines input parameters and material–process interaction in a generic printing information model for designing and planning concrete printing projects. Exchange requirements for concrete printing are identified and evaluated, serving as the basis for the printing information model. The printing information model, as a semantic (or meta) model, is conceptualized using object-oriented modeling concepts, formalized as an ontology, verified as an instantiable semantic model, and validated with a software tool developed as a plug-in for BIM platforms. As a result, a printing information model is developed to serve as a generally valid semantic model for the AM of concrete structures and has the potential to improve data modeling concepts currently deployed for concrete printing. [ABSTRACT FROM AUTHOR]

We introduce a theoretical model of information acquisition under resource limitations in a noisy environment. An agent must guess the truth value of a given Boolean formula f after performing a bounded number of noisy tests of the truth values of variables in the formula. We observe that, in general, the problem of finding an optimal testing strategy for f is hard, but we suggest a useful heuristic. The techniques we use also give insight into two apparently unrelated but well-studied problems: (1) rational inattention, that is, when it is rational to ignore pertinent information (the optimal strategy may involve hardly ever testing variables that are clearly relevant to f), and (2) what makes a formula hard to learn/remember. [ABSTRACT FROM AUTHOR]

Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the "Foundry," which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future. [ABSTRACT FROM AUTHOR]

Siamese-based RGBT trackers have attracted wide attention because of their high efficiency. However, there is a lack of an effective multimodal fusion module and information interaction between the search area and template area, which leads to poor performance of these siamese-based RGBT trackers. To solve this problem, inspire by the global information modeling capability of the transformer, we construct a siamese-based transformer RGBT tracker consisting of a single unified transformer module. Specifically, we propose a unified transformer fusion module to achieve feature extraction and global information interaction in the siamese RGBT tracker, i.e., the interaction between the search area and template area, the interaction between different modalities. It consists of self-attention and cross-attention, which are used to extract features and information interaction respectively. In addition, to alleviate the impact of multimodal fusion on the efficiency of template update in the tracking stage, we propose a feature-level template update strategy, which effectively improves tracking efficiency. To verify the effectiveness of our tracker, we evaluate it on five benchmark datasets including GTOT, RGBT210, RGBT234, LasHeR and VTUAV, and the results show that our tracker achieves excellent performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Demand planning in the semiconductor industry can be complicated due to challenges such as extended cycle times, rapid innovation cycles, and the Bullwhip Effect. Approaches that provide a deeper understanding of customer orders and their associated demand are crucial to enhance demand planning accuracy. Previous studies have employed convolutional neural networks (CNNs) on heat map representations of customer order transactions to effectively classify customer order behaviors (COBs), leading to improved insights into customer behavior. However, these approaches have primarily focused on analyzing customer order patterns without considering contextual information, such as financial or market-related data, which can benefit the classification process. Therefore, we propose a Semantic Context Information Modeling methodology for Neural Networks (SCIM-NN) based on ontologies, knowledge graph embeddings, and multi-stream neural networks to include context information for a classification task. We show the application of SCIM-NN on a use case in the domain of COB and evaluate the performance of the context-aware model on customer data of Infineon Technologies AG. Results indicate that including context information improves the overall classification performance compared to a benchmark CNN. [ABSTRACT FROM AUTHOR]

Data-driven intelligent methods have demonstrated their effectiveness in the area of fault diagnosis. However, most existing studies are based on the assumption that the distributions of normal and faulty samples are balanced during the diagnostic process. This assumption significantly decreases the application range of a diagnostic model as the samples in most real-world scenarios are highly unbalanced. To cope with the limitations caused by unbalanced data, this paper proposed an original dual-view network (DVN). Firstly, an interactive graph modeling strategy is introduced for relationship information modeling of multi-sensor data. Meanwhile, the graph convolution operation is used as the baseline for feature extraction of the constructed interactive graph to mine for fault representations. Secondly, an original dual-view classifier consisting of a binary classifier and a multi-class classifier is proposed, which divides fault diagnosis into two stages. Specifically, in the first stage, the binary classifier performs the binary inference from the view of fault detection. In the second stage, the multi-class classifier performs the full-state inference from the view of fine-grained fault classification. Then, based on the dual-view classifier, a weight activation module is designed to alleviate training bias toward majority classes by sample-level re-weighting. Finally, the diagnosis results can be obtained according to the output of the multi-class classifier. Fault diagnosis experiments using two different datasets with varying data unbalance ratios were conducted to validate the effectiveness of the proposed method. The superiority of the proposed DVN is verified through comparisons with state-of-the-art methods. The effectiveness of the DVN is further validated through ablation studies with some ablative models. The DVN code is available at: https://github.com/CQU-ZixuChen/DualViewNetwork. [ABSTRACT FROM AUTHOR]

Reinforced concrete bridges (RCBs) are essential elements of transportation infrastructure, necessitating ongoing inspection and maintenance to guarantee their durability and structural integrity. This research aims to develop an integrated framework for inspection and maintenance intervention in reinforced concrete bridges (RCB), leveraging the potential of as-is Bridge Information Modeling (BrIM). The methodology begins by converting 2D drawings into a 3D as-is BrIM model, facilitating a more accurate representation of the bridge's current state. This is followed by creating a comprehensive bridge inventory, allowing for meticulous tracking of vital components and their condition. Subsequently, inspection data are acquired using advanced sensing technologies, enabling real-time data collection and analysis. Structural defects are modeled on the as-is BrIM model to enhance diagnostic capabilities, providing a visual representation of areas requiring intervention. The framework also makes it easier to create reports, ensuring that important information is efficiently shared with the right people. A thorough case study using a concrete bridge is used to show the effectiveness of the created framework. The BrIM framework's potential to improve the bridge inspection and maintenance process was demonstrated by integrating inspection reporting with BrIM, which produced encouraging results. The research contribution to the body of knowledge is significant as the developed framework considerably enhances the administration of bridge inspection and maintenance procedures, resulting in greater efficacy by offering a thorough and clear picture of the bridge's current state. [ABSTRACT FROM AUTHOR]

Low-voltage distribution lines lack efficient monitoring. With the massive proliferation of distributed energy resource supply, it is necessary to establish an effective monitoring system for low-voltage distribution lines to ensure the electrical safety and continuity of power supply services. This paper proposes monitoring system for low-voltage distribution lines based on the Internet of things (IoT) technology. An IoT based monitoring system contains a large number of low-voltage monitoring terminals, different manufacturers, and different types of terminal equipment to solve the interconnection problem of information interaction. This paper first analyzes the terminal functional communication requirements based on IEC 61850 standards. Further, the information models required by the electrical volume collection and non-electric quantity collection functions are studied. The information modeling for different types of monitoring terminal equipment is presented and thus provide the configuration suggestions. The monitoring terminal based on the information model developed in this paper is applied in a university campus to realize the monitoring of low-voltage lines and distributed power supply. [ABSTRACT FROM AUTHOR]

This research aims to establish an optimized information foundation to bolster the effectiveness of financial intelligence management within the system of economic security. The chief scientific objective is to introduce an information algorithm, specifically designed for the management of financial intelligence, to fortify the economic security framework. The focal point of the research is the information support system pertaining to financial intelligence management. The research methodology is anchored in the application of contemporary information modeling methods, supplemented by functional algorithmization of processes. A modern graphic method is employed to enhance comprehensibility and accessibility. As an outcome of the study, a model of an information algorithm is presented, tailored to manage financial intelligence within the economic security system. However, the study acknowledges its limitations and does not incorporate all the elements of economic security assurance. Future research is recommended to delve into the specifics of information security within the financial intelligence management system. A distinct advantage of the proposed information algorithm lies in its graphic representation, enhancing the accessibility of the financial intelligence management system. The research scope is regional, indicating a limitation in the study. Future work should aim to expand the geographic applicability of these findings, enhancing the generalizability and relevance of the study. [ABSTRACT FROM AUTHOR]

Achieving fully autonomous driving requires seamless collaboration between advanced autonomous driving and road infrastructure technologies. As the proportion of autonomous vehicles (AVs) increases, challenges may arise from their insufficient knowledge of the behavior of traffic objects and inability to effectively drive short distances. Therefore, traffic control centers that can proactively control these issues in real time are essential. In this study, first, the terminology is defined and the types of AV-mixed Traffic Information that a traffic control center needs to efficiently collect, store, and analyze to accommodate the coexistence of AVs and conventional vehicles are identified. Second, a generic notation for an AV-mixed Traffic Information model is defined and the results of modeling each AV-mixed Traffic Information type are presented. Third, an AV-mixed Traffic Information services model that included the names, operations, input/output messages, and relationships of all services is suggested. Finally, the importance of the service functionalities is evaluated through a survey. This study will serve as an initial guideline for the design, construction, and operation of traffic control centers and will help proactively address issues that may arise from the interaction between AVs and conventional vehicles on the road. Moreover, it contributes to identifying the types of traffic information and services that traffic control centers must provide in the era of AV-mixed traffic and suggests future directions for analysis and utilization of traffic information. [ABSTRACT FROM AUTHOR]

Numerous factors affect the increased temperature of amachine tool, including prolonged and high-intensity usage, tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently, spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by the temperature rise of the Spindle fromaffecting the accuracy during the machining process, typically, the factory will warm up themachine before themanufacturing process. However, if there is noway to understand the tool spindle's thermal deformation, the machining quality will be greatly affected. In order to solve the above problem, this study aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practical application, only a few temperature sensors are used to input the information into the prediction model for realtime thermal displacement prediction. This approach has greatly improved the quality of tool processing. However, each algorithm has different performances in different environments. In this study, an ensemble model is used to integrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results show that the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms. [ABSTRACT FROM AUTHOR]