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1 |
Transformation of semantic knowledge into simulation-based decision support
تحول دانش معنایی به پشتیبانی تصمیم گیری مبتنی بر شبیه سازی-2021 Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, indepth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model.
This knowledge-intensity leads to long development times of respective projects, which contradicts the need
for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to
foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation
model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides
a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully
parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing
guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into
instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated
transformation of this conceptual model into an executable one. This novel modeling approach makes model
development more efficient by reducing its complexity. It is validated in a use case implementation from
semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate
the impacts of the COVID-19 pandemic on a global supply chain network.
keywords: تحول دانش | پشتیبانی تصمیم | هستی شناسی | مدل سازی ترکیبی | شبیه سازی همه گیر | شبیه سازی زنجیره تامین | Knowledge Transformation | Decision Support | Ontologies | Hybrid modeling | Pandemic Simulation | Supply chain simulation |
مقاله انگلیسی |
2 |
An intelligent semantic system for real-time demand response management of a thermal grid
یک سیستم معنایی هوشمند برای مدیریت پاسخ به تقاضای زمان واقعی یک شبکه حرارتی-2020 “Demand Response” energy management of thermal grids requires consideration of a wide range of factors at
building and district level, supported by continuously calibrated simulation models that reflect real operation
conditions. Moreover, cross-domain data interoperability between concepts used by the numerous hardware and
software is essential, in terms of Terminology, Metadata, Meaning and Logic. This paper leverages domain
ontology to map and align the semantic resources that underpin building and district energy management, with a
focus on the optimization of a thermal grid informed by real-time energy demand. The intelligence of the system
is derived from simulation-based optimization, informed by calibrated thermal models that predict the network’s
energy demand to inform (near) real-time generation. The paper demonstrates that the use of semantics helps
alleviate the endemic energy performance gap, as validated in a real district heating network where 36% reduction
on operation cost and 43% reduction on CO2 emission were observed compared to baseline operational
data. Keywords: Thermal grid | Demand response | Energy optimization | Operation cost | Data interoperability | Semantic ontology |
مقاله انگلیسی |
3 |
Micro-content Shortlisting Using Interactive AI Method
لیست کوتاه محتوای خرد با استفاده از روش هوش مصنوعی تعاملی-2020 The real-time detection of relevant information
and then shortlisting them on the user interfaces (UI) is a real
challenge. In this paper, we explain the use of Artificial
Intelligence (AI) and other methods for instantaneous
identification of information on the UI (web browser) and also a
system of injecting actionable buttons in an overlay on the
website for shortlisting. We also cover the patterns of the data,
and it’s dynamic validation in order to shortlist them in one
place as a universal list. Also making them accessible across
other websites with a consistent experience. Index Terms: shortlist | cross-domain | tagging | list | artificialintelligence |
مقاله انگلیسی |
4 |
Research on the application of block chain big data platform in the construction of new smart city for low carbon emission and green environment
تحقیق در مورد کاربرد بستر داده های بزرگ بلاک چین در ساخت شهر هوشمند جدید برای انتشار کربن کم و محیط سبز-2020 The sharing of government information resources is significant for improving the level of governance and
social information. However, due to the existence of cross-domain security and trust islands, government
departments are hindering the sharing of government information resources with other organizations and the
public. To this end, the blockchain technology is used to construct a decentralized distributed peer-to-peer
trust service system, which is integrated with the existing PKI/CA security system to establish a new trust
model that supports multi-CA coexistence. Based on this, the structural composition and functional data flow
of the blockchain smart city information resource sharing and exchange model designed in this paper. This
paper launched a study on the role of the smart big data platform, and selected the development of smart cities
in Hefei as an empirical analysis. From the connotation of smart city, block chain and big data technology
combined, and the positive effects of relevant information technology summarized on the construction of smart
city big data platform. Based on this, the smart city development level evaluation model of TOPSIS method
constructed. The evaluation model constructed to make a vertical comparison from 2012 to 2017, the scale of
smart cities is growing at an average annual rate of more than 30%, saving 20% of urban resource allocation
and becoming a new pillar industry. Therefore, Hefei City should further increase environmental supervision
and promote the use of low-carbon environmental protection new energy. The improvement of government
management level has a positive effect on the construction of smart Hefei Keywords: Block chain | PKI/CA | New smart city | Government information |
مقاله انگلیسی |
5 |
A deep learning framework for Hybrid Heterogeneous Transfer Learning
یک چارچوب یادگیری عمیق برای یادگیری انتقال ناهمگن ترکیبی-2019 Most previous methods in heterogeneous transfer learning learn a cross-domain feature mapping between different domains based on some cross-domain instance-correspon-dences. Such instance-correspondences are assumed to be representative in the source domain and the target domain, respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be pre-cise, and thus the transformed source-domain labeled data using the feature mapping are not useful to build an accurate classifier for the target domain. In this paper, we offer a new heterogeneous transfer learning framework named Hybrid Heterogeneous Transfer Learning (HHTL), which allows the selection of corresponding instances across domains to be biased to the source or target domain. Our basic idea is that though the correspond-ing instances are biased in the original feature space, there may exist other feature spaces, projected onto which, the corresponding instances may become unbiased or representa-tive to the source domain and the target domain, respectively. With such a representation, a more precise feature mapping across heterogeneous feature spaces can be learned for knowledge transfer. We design several deep-learning-based architectures and algorithms that enable learning aligned representations. Extensive experiments on two multilingual classification datasets verify the effectiveness of our proposed HHTL framework and algo-rithms compared with some state-of-the-art methods. Keywords: Heterogeneous transfer learning | Deep learning | Multilingual text classification |
مقاله انگلیسی |
6 |
Domain-specific data mining for residents transit pattern retrieval from incomplete information
استخراج داده های خاص دامنه برای بازیابی الگوی ترانزیت ساکنان از اطلاعات ناقص-2019 The rapid development of Cyber-Physical System (CPS) is gradually cultivating a smart world based on Big
Data and computational infrastructures. Such reformation has initiated revolutions in various industries and is
gradually reshaping our daily routines. Among these trends, the data-driven intelligentization in Urban Public
Transportation Systems can bring the most significant impact to our society, because they provide pervasive
reachability. Optimizing and constructing such systems, which requires deep insights into passenger behavioral
patterns, is a highly domain-specific data mining task. Although computational infrastructures have provided
sufficient processing capacity, the practical utilization of such data is facing several challenges: information
alignment in heterogeneous data sources and information enrichment for domain-specific applications. In this
article, we first propose a cross-domain method to increase the usability of data by removing the inconsistency
in vehicles’ positioning and passengers’ transaction data. We then use rule-based methods to reconstruct latent
mobility information, thereby enabling small grained trajectory based applications. Finally, we provide use cases
using the reconstructed information to derive insights to the public transportation system of our target city. Our
work can serve as a domain-specific information enrichment and data mining framework for CPS in smart cities. Keywords: Data mining | Public transit | Data enrichment | Smart city |
مقاله انگلیسی |
7 |
CD-CARS: Cross-domain context-aware recommender systems
CD-CARS: سیستم های توصیه گر متشکل از آگاهی از حوزه متقابل-2019 In this paper, we address two research topics in Recommender Systems (RSs) which have been developed in parallel without a deeper integration: Cross-Domain RS (CDRS) and Context-Aware RS (CARS). CDRS have emerged to enhance the quality of recommendations in a target domain by leveraging sources of information in different domains. CDRS are especially useful to address cold-start, sparsity and diversity problems in target domains with scarce information. CARS, on its turn, have been proposed to consider contextual information for recommendations. Such systems are suitable when the users’ interests change according to factors like time, location, among others. By combining these two approaches, better RSs can be developed, considering both the availability of useful data from multiple domains and the use of con- textual information. In this paper, we formalize the combination of CDRS and CARS, which represents a more systematic integration of these approaches compared to previous work. Based on this formulation, we developed novel RSs techniques, named CD-CARS. To evaluate the developed CD-CARS techniques, we performed extensive experimentation through real datasets taking into account several scenarios. The recommendations were evaluated in terms of predictive and ranking performance, respectively achieving up to 62.6% and 45%, depending on the scenario, in comparison to traditional cross-domain collaborative filtering techniques. Therefore, the experimental results have shown that the integration of techniques de- veloped in isolation can be useful in a variety of situations, in which recommendations can be improved by information gathered from different sources and can be refined by considering specific contextual in- formation. Keywords: Cross-domain recommendation | Context-aware recommendation | Collaborative filtering recommendation | Cross-domain context-aware | recommendation |
مقاله انگلیسی |
8 |
From big data to smart energy services: An application for intelligent energy management
از داده های بزرگ به سرویس های هوشمند انرژی: یک برنامه کاربردی برای مدیریت انرژی هوشمند-2018 Big data is an ascendant technological concepts and includes smart energy services, such as intelligent
energy management, energy consumption prediction and exploitation of Internet of Things (IoT) solutions.
As a result, big data technologies will have a significant impact in the energy sector. This paper proposes a
high level architecture of a big data platform that can support the creation, development, maintenance and
exploitation of smart energy services through the utilisation of cross-domain data. The proposed platform
enables the simplification of the procedure followed for the information gathering by multiple sources,
turning into actionable recommendations and meaningful operational insights for city authorities and local
administrations, energy managers and consultants, energy service companies, utilities and energy providers.
Α web-based Decision Support System (DSS) has been developed according to the proposed architecture,
exploiting multi-sourced data within a smart city context towards the creation of energy management action
plans. The pilot application of the developed DSS in three European cities is presented and discussed. This
“data-driven” DSS can support energy managers and city authorities for managing their building facilities’
energy performance.
Keywords: Big Data; Decision Support System; Energy Services; Intelligent Management; Smart Cities. |
مقاله انگلیسی |
9 |
Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system
ذخیره سازی و سیستم دسترسی خود تطبیقی داده های بزرگ سلامت مبتنی اینترنت اشیا هوشمند-2018 In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with
self-adaptive access control is proposed. The aim is to ensure the security of patients’
healthcare data, realize access control for normal and emergency scenarios, and support
smart deduplication to save the storage space in big data storage system. The medical files
generated by the healthcare IoT network are encrypted and transferred to the storage sys
tem, which can be securely shared among the healthcare staff from different medical do
mains leveraging a cross-domain access control policy. The traditional access control tech
nology allows the authorized data users to decrypt patient’s sensitive medical data, but
also hampers the first-aid treatment when the patient’s life is threatened because the on
site first-aid personnel are not permitted to get patient’s historical medical data. To deal
with this dilemma, we propose a secure system to devise a novel two-fold access control
mechanism, which is self-adaptive for both normal and emergency situations. In normal
application, the healthcare staff with proper attribute secret keys can have the data ac
cess privilege; in emergency application, patient’s historical medical data can be recovered
using a password-based break-glass access mechanism. To save the storage overhead in
the big data storage system, a secure deduplication method is designed to eliminate the
duplicate medical files with identical data, which may be encrypted with different access
policies. A highlight of this smart secure deduplication method is that the remaining med
ical file after the deduplication can be accessed by all the data users authorized by the
different original access policies. This smart healthcare big data storage system is formally
proved secure, and extensive comparison and simulations demonstrate its efficiency.
Keywords: Privacy-preserving ، Healthcare big data storage ، Internet-of-things ، Self-adaptive access control ، Smart deduplication |
مقاله انگلیسی |
10 |
Cross-domain negative effect of work-family conflict on project citizenship behavior: Study on Chinese project managers
تاثیر منفی دامنه ای تعارض کار با خانواده روی رفتار شهروندی پروژه: مطالعه روی مدیران پروژه چینی-2018 This research aims to examine whether and how the bidirectional work-family conflict—work-to-family conflict (WFC) and family-to-work conflict (FWC)—would influence project citizenship behavior (PCB) among Chinese project managers. We proposed hypotheses regarding the relationships between work-family conflict and PCB and the mediating effects of project commitment, which considered the role of national context. Data collected from 154 Chinese project managers were analyzed using structural equation modeling. It was found that FWC had negative relationships with all the three chosen PCBs, i.e., helping behavior, individual initiative, and relationship maintenance, and project commitment mediated these relationships. However, no negative influences of WFC on the three PCBs and project commitment were found. Further comparisons of effects of WFC and FWC on PCBs and project commitment indicated that Chinese project managers were less subject to the negative impacts of WFC. Overall, our results supported the cross-domain negative effect but rejected matching-domain negative effect of work-family conflict among Chinese project managers. We extend understandings of work-family conflict and PCB in the project context, and verify the importance of national context in interpreting work-family issues. Practical suggestions are also discussed regarding increasing project managers PCB.
keywords: Project manager |National context |Work-family conflict |Project citizenship behavior |Project commitment |
مقاله انگلیسی |