Analysis of the Risk Path of the Pipeline Corridor Based on System Dynamics

Under the background of China’s continuous promotion of urbanization, urban underground integrated pipeline corridor has become an inevitable trend of future urban integrated management. After the completion of the pipeline corridor, how to effectively manage its risks in operation and maintenance management has become a topic at this stage. In this paper, through the combination of the classical AHP method and DSM method, based on a large number of literature studies, the risk relationship system of the integrated pipe corridor is constructed. AnyLogic software is applied to simulate the system dynamics, analyze the impact of dynamic changes of each risk factor on the risk accident of the integrated pipe corridor, carry out uncertainty reasoning from multiple perspectives, and realize the evaluation and analysis of the accident risk of the integrated pipe corridor. The results of the study could provide targeted support tools for integrated pipeline corridor risk operation and maintenance management.

Cloud model-based evaluation of landslide dam development feasibility

As natural backwater structures, landslide dams both threaten downstream human settlement or infrastructure and contain abundant hydro-energy and tourism resources, so research on their development feasibility is of great significance for permanently remedying them and effectively turning disasters into benefits. Through an analysis of the factors influencing landslide dam development and utilization, an index system (consisting of target, rule, and index layers) for evaluating development feasibility was constructed in this paper. Considering uncertainty and randomness in development feasibility evaluation, a cloud model-improved evaluation method was proposed to determine membership and score clouds based on the uncertainty reasoning of cloud model, and a cloud model-improved analytic hierarchy process (AHP-Cloud Model) was introduced to obtain weights. Final evaluation results were obtained using a hierarchical weighted summary. The improved method was applied to evaluate the Hongshiyan and Tangjiashan landslide dams and the results were compared with the maximum membership principle results. The results showed that the cloud model depicted the fuzziness and uncertainty in the evaluation process. The improved method proposed in this paper overcame the loss of fuzziness in the maximum membership principle evaluation results, and was capable of more directly presenting evaluation results. The development feasibility of the Hongshiyan landslide dam was relatively high, while that of the Tangjiashan landslide dam was relatively low. As suggested by these results, the evaluation model proposed in this paper has great significance for preparing a long-term management scheme for landslide dams.

Shared learning activity labels across heterogeneous datasets

Nowadays, the advancement of sensing and communication technologies has led to the possibility of collecting a large amount of sensor data, however, to build a reliable computational model and accurately recognise human activities we still need the annotations on sensor data. Acquiring high-quality, detailed, continuous annotations is a challenging task. In this paper, we explore the solution space on sharing annotated activities across different datasets in order to enhance the recognition accuracies. The main challenge is to resolve heterogeneity in feature and activity space between datasets; that is, each dataset can have a different number of sensors in heterogeneous sensing technologies and deployed in diverse environments and record various activities on different users. To address the challenge, we have designed and developed sharing data and sharing classifiers algorithms that feature the knowledge model to enable computationally-efficient feature space remapping and uncertainty reasoning to enable effective classifier fusion. We have validated the algorithms on three third-party real-world datasets and demonstrated their effectiveness in recognising activities only with annotations from as little as 0.1% of each dataset.

A Decision Making Approach Based on Hesitant Fuzzy Linguistic-Valued Credibility Reasoning

Credibility reasoning has attracted a lot of attention due to its distinguished power and efficiency in representing uncertainty and vagueness within the process of reasoning and decision making. Aiming at the problem of inaccurate credibility estimation in uncertainty reasoning and making experts to express hesitant preferences better in evaluation reasoning process, this paper introduces hesitant fuzzy linguistic term set into credibility uncertainty reasoning. First, we propose hesitant fuzzy linguistic-valued credibility (HLCF), and establish the knowledge representation model of the hesitant fuzzy linguistic-valued credibility. Then, in order to solve the problem of incomplete information in the evaluation reasoning process, an information complement algorithm based on maximum similarity is constructed. After that, the algorithms of single rule and multiple rules of parallel relationship of hesitant fuzzy linguistic-valued credibility are proposed to enrich the reasoning rule base and get more accurate reasoning results. The closeness degrees between the conclusions of each alternative after reasoning and the expected value are calculated, so as to select the most suitable alternative. Finally, a practical example which concerned the social risk analysis is given to illustrate the applicability and effectiveness of the proposed approach.

Fusion and Correction of Multi-Source Land Cover Products Based on Spatial Detection and Uncertainty Reasoning Methods in Central Asia

Land cover products are an indispensable data source in land surface process research, and their accuracy directly affects the reliability of related research. Due to the differences in factors such as satellite sensors, the temporal–spatial resolution of remote sensing images, and landcover interpretation technologies, various recently released land cover products are inconsistent, and their accuracy is usually insufficient to meet application requirements. This study, therefore, established a fusion and correction method for multi-source landcover products by combining them with landcover statistics from the Food and Agriculture Organization of the United Nations (FAO), introducing a spatial consistency discrimination technique, and applying an improved Dempster-Shafer evidence fusion method. The five countries in Central Asia were used for a method application and verification assessment. The nine products selected (CCI-LC, CGLS, FROM-GLC, GLCNMO, MCD12Q, GFSAD30, PALSAR, GSWD, and GHS-BUILT) were consistent in time and covered the study area. Based on the interpretation of 1437 high-definition image verification areas, the overall accuracy of the fusion landcover result was 85.32%, and the kappa coefficient was 0.80, which was better than that of the existing comprehensive products. The spatial consistency fusion method had the advantage of an improved statistical fitting, with an overall similarity statistic of 0.999. The improved Dempster-Shafer evidence theory fusion method had an accuracy that was 4.86% higher than the spatial consistency method, and the kappa coefficient increased by 0.07. Combining these two methods improved the consistency of the multi-source data fusion and correction method established in this paper and will also provide more reliable basic data for future research in Central Asia.

A Privacy-Preserving Personalized Service Framework through Bayesian Game in Social IoT

It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.

The Usage of Probability Theory Concepts for Analysing Big Data Uncertainty Reasoning

Partial observability, nondeterminism or a combination of the two develop the problem of uncertainty a common occurrence in big data. An agent is needed to handle this uncertainty. This paper aims to see how an agent can tame uncertainty with the degree of belief and to design an agent program that implements the agent function, the mapping from percepts to actions, especially in the field of big data where volumes of data needs to be handled. The agent program more often takes the current percept as input from the sensors and return an action. Uncertainty arises because of ignorance or volumes of data; The agent’s lack to express the truth of the event in the sentence due to uncertainty that prevails, which can be expressed using probability. Probabilities summarises the agent’s belief relative to the evidence and probability distribution is used to specify the probability that exist in assigning to any random variables. Partial observability of the world brings in unobserved aspects, these can be resolved by estimating the values using probability, that help better agent decision in any field including big data. The agent program come into being through learning methods. An agent is designed to form representations of a complex world, the world with huge voluminous data, use a process of inference to derive new representations about the world, and use these new representations to deduce what to do.