A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area

Portuguese SKA white book

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Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Transport 2040 : Impact of Technology on Seafarers - The Future of Work

https://commons.wmu.se/lib_reports/1091/thumbnail.jp

Recent advances in low-cost particulate matter sensor: calibration and application

Particulate matter (PM) has been monitored routinely due to its negative effects on human health and atmospheric visibility. Standard gravimetric measurements and current commercial instruments for field measurements are still expensive and laborious. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with insufficient spatial resolution. The new trends of PM concentration measurement are personalized portable devices for individual customers and networking of large quantity sensors to meet the demand of Big Data. Therefore, low-cost PM sensors have been studied extensively due to their price advantage and compact size. These sensors have been considered as a good supplement of current monitoring sites for high spatial-temporal PM mapping. However, a large concern is the accuracy of these low-cost PM sensors. Multiple types of low-cost PM sensors and monitors were calibrated against reference instruments. All these units demonstrated high linearity against reference instruments with high R2 values for different types of aerosols over a wide range of concentration levels. The question of whether low-cost PM monitors can be considered as a substituent of conventional instruments was discussed, together with how to qualitatively describe the improvement of data quality due to calibrations. A limitation of these sensors and monitors is that their outputs depended highly on particle composition and size, resulting in as high as 10 times difference in the sensor outputs. Optical characterization of low-cost PM sensors (ensemble measurement) was conducted by combining experimental results with Mie scattering theory. The reasons for their dependence on the PM composition and size distribution were studied. To improve accuracy in estimation of mass concentration, an expression for K as a function of the geometric mean diameter, geometric standard deviation, and refractive index is proposed. To get rid of the influence of the refractive index, we propose a new design of a multi-wavelength sensor with a robust data inversion routine to estimate the PM size distribution and refractive index simultaneously. The utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes. Furthermore, for the outdoor environment, data reported by low-cost sensors were compared against satellite data. The remote sensing data could provide a daily calibration of these low-cost sensors. On the other hand, low-cost PM sensors could provide better accuracy to demonstrate the microenvironment

Damage detection in reinforced concrete square slabs using modal analysis and artifical neural network

Reinforced concrete (RC) structures are usually subjected to various types of loadings, such as permanent, sustained and transient during their lifetime. Reinforced concrete slabs are one of the most fundamental structural elements in buildings and bridges, which might be exposed to unfavourable conditions such as, impaired quality control, lack of maintenance, adverse environmental effects, and inadequate initial design. Therefore, the resistant capacity of the affected elements would dramatically be reduced which most likely leads to the partial or whole collapse of the structure. Non-destructive testing (NDT) techniques can be used to inspect for defects without further damaging the tested component. Significant research and development have been conducted on the performance of vibration characteristics to identify damage in different types of structures. The vibrations based damage detection methods, particularly modal based methods, are found to be promising in evaluating the health condition of a structure in terms of detection, localisation, classification and quantification of the potential damage in the structure. Damage in composites and the non-homogeneous material is tricky to assess from a surface inspection alone. Although the development of NDTs, especially experimental modal analysis (EMA), has been pushed forward by the aerospace industry where composites materials are employed in many safety critical applications, EMA is not widely employed to diagnose all types of RC structural members. Damage detection in reinforced concrete square slabs is the primary aim of this study. This is achieved experimentally using experimental modal analysis (EMA) and numerically using finite element method (FEM). Artificial neural network (ANN) is also used in this study to classify the void sizes. A whole testing procedure of EMA on freely supported slab was established in this research. It is based on impact hammer technique, as a relevant excitation source for field measurements. After the quality of the measurements had been ensured, the experimental data was collected from four pairs laboratory-scale reinforced concrete slabs modelled with various ranges of parameters. After collecting data, Matlab software was employed to obtain modal parameters, such as natural frequencies, mode shapes and modal damping ratios from two RC square slabs. EMA and FEM studies were undertaken to assess and improve modelling technique for capturing the aim. FEM was used to model the RC slabs using commercial ANSYS software. To balance model simplicity of RC slabs with the ability to reliably predict their dynamic response, both predicted and measured dynamic results were compared to ensure that the analytical model represents the experimental results with reasonable accuracy. ANSYS software was also employed to numerically extract the natural frequencies of the slab. Then, using Matlab software, the extracted natural frequencies were fed as the input to the ANN to classify the void sizes in the slab. The dynamic properties of the slab were investigated for each of four pairs to evaluate modal parameters (natural frequencies, damping ratio and mode shapes) sensitivity to slab's dimensions, degree of damage owing to incremental loading and induced void. The performance of EMA based on impact hammer technique was credibly tested and verified on measurements, which were collected from eight slabs with various parameters. EMA efficiency was conclusively proved on data from modal parameters sensitivity to slab's dimensions, incremental loading and induced void. The results indicated that using a bigger reinforced concrete slabs (1200 x 1200 mm2) could potentially have further reduced the discrepancy between theoretical (analytical and numerical) and experimental natural frequencies than smaller slabs (600 x 600 mm2). In general, for the specimens tested slabs, natural frequencies were more sensitive to the damage introduced than the damping ratio because the damping did not consistently increase or decrease as damage increased. The changes in mode shapes tended to increase with increasing damage level. Even small damage induced poised changes to the mode shapes, but it may not be obvious visually. Utilising sophisticated methods for damage identification, which are vital steps in higher level of damage detection in structures, is one of the major contributions to the knowledge. The proposed Modal Assurance Criterion (MAC) and Coordinate Modal Assurance Criterion (COMAC) techniques as advanced statistical classification model were employed in this study. From the vibration mode shapes induced void location can be identified via MAC and COMAC techniques when both intact and damaged data were compared. MAC provided a clear change in the mode shape while the COMAC provided the change in specific a location whereby the location of damage was identified. The outcomes of this two techniques can show the realistic location of the void. Beside the aforementioned contributions in this research, the feasibility of a Feed-Forward Back Propagation Neural Network (FFBPNN) was investigated using ten natural frequencies as input and the void sizes as output. Excellent results were obtained for damage identification of four void sizes, showing that the proposed method was successfully developed for damage detection of slabs. The results proved that the precision of the models was reduced when dealing with small size void. The large size void was detected more accurately than small size void as expected. This is because the natural frequencies of the small void of different location attributed together. Therefore, natural frequencies alone were not considerably good enough to make good identifications for small size void. Moreover, the natural frequencies set of three untrained void specifications were used as FFBPNN inputs to test the performance of the neural networks. The obtained results show that the proposed network can predict the void specifications of the unseen data with high accuracy. Overall, the methodology followed in this work for damage detection in reinforced concrete square slabs is novel when compared to the breadth and depth of all other previous works carried out in the field of reinforced concrete structures

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words