A Universal Predictive Mobility Management Scheme for Urban Ultra-dense Networks with Control/Data Plane Separation

Sun, Yang; Chang, Yanxun; Hu, Mengshi; Zeng, Tianyi

doi:10.1109/access.2017.2694863

Cited by 28 publications

(13 citation statements)

References 19 publications

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“…Therefore, in the experimental simulation stage, the target purity in the E effluent and the impurity purity in the R effluent are selected as the output. In order to measure the performance of the predictive models, several performance indicators are defined below, whereŷ is the estimated value and y is the actual value [32]. The model performance indicators are defined as shown in Table 2.…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Soft Sensing Modeling of the SMB Chromatographic Separation Process Based on the Adaptive Neural Fuzzy Inference System

Wang

et al. 2019

Journal of Sensors

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Simulated moving bed (SMB) chromatographic separation technology is a new adsorption separation technology with strong separation ability. Based on the principle of the adaptive neural fuzzy inference system (ANFIS), a soft sensing modeling method was proposed for realizing the prediction of the purity of the extract and raffinate components in the SMB chromatographic separation process. The input data space of the established soft sensor model is divided, and the premise parameters are determined by utilizing the meshing partition method, subtractive clustering algorithm, and fuzzy C-means (FCM) clustering algorithm. The gradient, Kalman, Kaczmarz, and PseudoInv algorithms were used to optimize the conclusion parameters of ANFIS soft sensor models so as to predict the purity of the extract and raffinate components in the SMB chromatographic separation process. The simulation results indicate that the proposed ANFIS soft sensor models can effectively predict the key economic and technical indicators of the SMB chromatographic separation process.

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Soft Sensing Modeling of the SMB Chromatographic Separation Process Based on the Adaptive Neural Fuzzy Inference System

Wang

et al. 2019

Journal of Sensors

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“…The authors proposed a temporal Euclidean embedding model (TEE) which uses the relationship between users and items logically and generates speedy recommendations based on the user's current need. [2] proposed a unique approach to improve the performance of RS by embedding temporal dynamics, reviews and item correlation with the help of TmRevCo which is inspired from CoFactor model [92] that decomposes the rating matrix jointly with the item co-occurrence matrix which share the same item latent factors.…”

Section: Time-dependent Modelmentioning

confidence: 99%

“…In recent times, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users' needs through mapping the available products to users based on their interests towards items [1]. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended [2]. This complex and dynamic data characteristic brought a big challenge in producing accurate recommendations as a result of these shifts in the relationship between users and items to be recommended [3].…”

Section: Introductionmentioning

confidence: 99%

“…In response to these dynamic relationships between users and items to be recommended, dynamic recommender systems (DRSs) were built to improve recommendation accuracy [4]. DRSs are kinds of systems that were intended to capture the temporal changes within different spheres, such as user or items related data as well as other time changing phenomena, by modifying their recommendations to meet the users' needs accordingly [2]. The recent progress in DRSs is motivated following the success of [5] in the Netflix movie competition held in 2009.…”

Section: Introductionmentioning

confidence: 99%

“…It was proven that the incorporation of these factors makes Netflix s algorithm to perform best for rating predictions. Subsequently, many of the research works have been carried out to consider more types of concept drifts in RS [2,[5][6][7][8]. For example, [9] proposed a framework for temporal RS over tweet stream which takes into account sudden shifts of users' interests and popularity of topics as time passes by.…”

Section: Introductionmentioning

confidence: 99%

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Recommender System Based on Temporal Models: A Systematic Review

et al. 2020

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Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.

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Mobility Prediction Based Resource Management

Imran¹,

Öztürk²,

Abubakar³

et al. 2020

Wiley 5G Ref

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Owing to the challenging nature of emerging applications, such as augmented reality, 4 K video streaming, and remote surgery, 5G networks are expected to deal with a broad range of issues including higher data rate provision, less energy consumption, lower latency, etc. Therefore, intelligent and proactive network management is envisioned to be an integral part of 5G in order to make the communication networks more agile, dynamic, and efficient. In this regard, mobility prediction has gained a significant amount of attention owing to their diverse application domains. This article focuses on discussing the implementation of mobility prediction for resource management in mobile communication networks. After describing the challenges expected from 5G networks with related statistical facts, predictive mobility management is introduced with its distinctive applications. After that, the use of mobility prediction in communication networks is elaborated according to different resource types, such as radio, energy, and built‐in resources, and the corresponding state‐of‐the‐art literature is classified accordingly.

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A Universal Predictive Mobility Management Scheme for Urban Ultra-dense Networks with Control/Data Plane Separation

Cited by 28 publications

References 19 publications

Soft Sensing Modeling of the SMB Chromatographic Separation Process Based on the Adaptive Neural Fuzzy Inference System

Soft Sensing Modeling of the SMB Chromatographic Separation Process Based on the Adaptive Neural Fuzzy Inference System

Recommender System Based on Temporal Models: A Systematic Review

Mobility Prediction Based Resource Management

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