Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study

Singh, Ravinder; Zhang, Yanchun; Wang, Hua; Miao, Yuan; Ahmed, Khandakar

doi:10.4108/eai.26-10-2020.166767

Cited by 16 publications

(12 citation statements)

References 53 publications

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“…Thanks to the designed services, the operators' decisions are not arbitrary or solely based on their experience, but they take into account the state of the system when the services are executed. In the future, the approach will be used to design a knowledge base, which contains a proper formalisation of operators' experience and their behaviour when interacting with the CPPS [39].…”

Section: Discussion About Experimental Evaluationmentioning

confidence: 99%

Context-Based Resilience in Cyber-Physical Production System

2021

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Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

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Section: Discussion About Experimental Evaluationmentioning

confidence: 99%

Context-Based Resilience in Cyber-Physical Production System

2021

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“…There are many reasons and explanations for this such as in regard to banks, safety and security may be the main cause. On the other side, the government can utilize the information and knowledge collected from such public data sets for national security purposes [54]. Apart form this the data can also be utilized to conduct largescale mobility and behaviour studies [55].…”

Section: Challenges With Ai Adoption For Smart Citiesmentioning

confidence: 99%

Bibliometric Mapping of Trends, Applications and Challenges of Artificial Intelligence in Smart Cities

Harnal

Sharma²,

Malik

et al. 2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affordable cost. Smart Cities are part of the solution to the growing challenges of urbanization. The adoption of new technologies like artificial intelligence (AI) is transforming cities, making them smarter, faster, and predicting opportunities for improvement. OBJECTIVES: This study is conducting a detailed bibliometric survey to investigate the applications and trends of Artificial Intelligence research for different areas of smart cities and emphasizing the potential effects and challenges of AI adaptation in smart cities over the past 30.5 years. METHODS: For this study, the Scopus database was used to collect a total of 1925 documents published between 1991-2021 (July). The bibliometric analysis includes document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of most cited documents. RESULTS: The analyzed findings of this research study reflect not only the significance of AI technology for various applications within numerous sectors in the smart city but also major obstacles in AI research for various sectors of smart cities. CONCLUSION: The research demonstrates that AI has the ability to construct today’s and tomorrow’s smart cities, but that each region’s potentials, conditions, and circumstances must be addressed in order to achieve a smooth internet city development.

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“…The energy function is constructed using boundary information in active contour and level set approaches, and the performance is dependent on the original curve. Graph is another major unsupervised segmentation technique [ 45 , 46 ]. To develop a globally optimal solution, graph-based segmentation uses region and boundary information [ 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic breast lesion segmentation in phase preserved DCE-MRIs

Pandey

Wang

Yin

et al. 2022

Health Inf Sci Syst

Self Cite

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We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.

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Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study

Cited by 16 publications

References 53 publications

Context-Based Resilience in Cyber-Physical Production System

Context-Based Resilience in Cyber-Physical Production System

Bibliometric Mapping of Trends, Applications and Challenges of Artificial Intelligence in Smart Cities

Automatic breast lesion segmentation in phase preserved DCE-MRIs

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