To uncover the inconsistencies among different kernel functions, two special cases of Eringen’s nonlocal theory, including the TPNI model and a modified model proposed by Eptaimeros are employed in ...the current investigation. Current research makes use of the integral form of Eringen’s nonlocal theory instead of the popular differential one to avoid the potential paradoxes. The Timoshenko beam theory is adopted to model rotating nanobeams with and without nonzero setting angles. Considering the number of existing applications of the element-free Galerkin method on nonlocal nanoscale beams are few, the element-free Galerkin method has been employed to develop numerical solutions. Free vibration frequencies of rotating nanobeams and local beams for varying rotating angular velocities have been studied. During the analyses of rotating effects, the factor of size effect is discussed. Size effects of rotating nanobeams for varying rotating angular velocities are examined. The impact on size effects contributed by rotating effects is evaluated. By comparing the vibration frequencies of two nonlocal models, inconsistencies between different kernel functions are uncovered.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Understanding the pattern of financial activities and predicting their development and changes are research hotspots in academic and financial circles. Because financial data contain complex, ...incomplete and fuzzy information, predicting their development trends is an extremely difficult challenge. Fluctuations in financial data depend on a myriad of correlated constantly changing factors. Therefore, predicting and analysing financial data are a nonlinear, time-dependent problem. Deep neural networks (DNNs) combine the advantages of deep learning (DL) and neural networks and can be used to solve nonlinear problems more satisfactorily compared to conventional machine learning algorithms. In this paper, financial product price data are treated as a one-dimensional series generated by the projection of a chaotic system composed of multiple factors into the time dimension, and the price series is reconstructed using the time series phase-space reconstruction (PSR) method. A DNN-based prediction model is designed based on the PSR method and a long- and short-term memory networks (LSTMs) for DL and used to predict stock prices. The proposed and some other prediction models are used to predict multiple stock indices for different periods. A comparison of the results shows that the proposed prediction model has higher prediction accuracy.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Building extraction from remote sensing (RS) images is a fundamental task for geospatial applications, aiming to obtain morphology, location, and other information about buildings from RS images, ...which is significant for geographic monitoring and construction of human activity areas. In recent years, deep learning (DL) technology has made remarkable progress and breakthroughs in the field of RS and also become a central and state-of-the-art method for building extraction. This paper provides an overview over the developed DL-based building extraction methods from RS images. Firstly, we describe the DL technologies of this field as well as the loss function over semantic segmentation. Next, a description of important publicly available datasets and evaluation metrics directly related to the problem follows. Then, the main DL methods are reviewed, highlighting contributions and significance in the field. After that, comparative results on several publicly available datasets are given for the described methods, following up with a discussion. Finally, we point out a set of promising future works and draw our conclusions about building extraction based on DL techniques.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Water is the source of human life and water pollution is becoming more and more serious with the development of cities. The supervision and treatment of water resources have become a big problem of ...urban development. Water quality monitoring is not timely, flood warning is not timely is directly related to the livelihood of the people. And the development of smart water utilities can solve problems timely and accurately. By placing water quality sensors in the urban water supply network, real-time monitoring of water quality can be performed to prevent incidents of drinking water pollution. After an incident of drinking water pollution occurs, reverse locating the pollution source through the information detected by the water quality sensors represents a challenging problem because in the actual water supply network, the direction and speed of the water flow will change with the water demand of the residents, thus leading to uncertainty in this problem. In conventional studies of pollution source location problems, it is often assumed that the water demand is fixed. However, due to the variability of the water demand of residents, this problem is actually a dynamic change problem and thus can be considered as a dynamic optimization problem. In this study, a Poisson distribution model was used to simulate the change of water demand among urban residents. On this basis, we proposed an improved genetic algorithm to solve the pollution source location problem and implemented two different water supply networks to perform the simulation experiments, which could accurately locate the pollution sources. The simulation results were compared with the standard genetic algorithm to verify the accuracy and robustness of the proposed algorithm.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Reducing pollutant detection time based on a reasonable sensor combination is desirable. Clean drinking water is essential to life. However, the water supply network (WSN) is a vulnerable target for ...accidental or intentional contamination due to its extensive geographic coverage, multiple points of access, backflow, infrastructure aging, and designed sabotage. Contaminants entering WSN are one of the most dangerous events that may cause sickness or even death among people. Using sensors to monitor the water quality in real time is one of the most effective ways to minimize negative consequences on public health. However, it is a challenge to deploy a limited number of sensors in a large-scale WSN. In this study, the sensor placement problem (SPP) is modeled as a sequential decision optimization problem, then an evolutionary reinforcement learning (ERL) algorithm based on domain knowledge is proposed to solve SPP. Extensive experiments have been conducted and the results show that our proposed algorithm outperforms meta-heuristic algorithms and deep reinforcement learning (DRL).
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When drinking water flows into the water distribution network from a reservoir, it is exposed to the risk of accidental or deliberate contamination. Serious drinking water pollution events can ...endanger public health, bring about economic losses, and be detrimental to social stability. Therefore, it is obviously crucial to research the water contamination source identification problem, for which scholars have made considerable efforts and achieved many advances. This paper provides a comprehensive review of this problem. Firstly, some basic theoretical knowledge of the problem is introduced, including the water distribution network, sensor system, and simulation model. Then, this paper puts forward a new classification method to classify water contamination source identification methods into three categories according to the algorithms or methods used: solutions with traditional methods, heuristic methods, and machine learning methods. This paper focuses on the new approaches proposed in the past 5 years and summarizes their main work and technical challenges. Lastly, this paper suggests the future development directions of this problem.
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Seismic exploration is a method of oil exploration that uses seismic information; that is, according to the inversion of seismic information, the useful information of the reservoir parameters can be ...obtained to carry out exploration effectively. Pre-stack data are characterised by a large amount of data, abundant information, and so on, and according to its inversion, the abundant information of the reservoir parameters can be obtained. Owing to the large amount of pre-stack seismic data, existing single-machine environments have not been able to meet the computational needs of the huge amount of data; thus, the development of a method with a high efficiency and the speed to solve the inversion problem of pre-stack seismic data is urgently needed. The optimisation of the elastic parameters by using a genetic algorithm easily falls into a local optimum, which results in a non-obvious inversion effect, especially for the optimisation effect of the density. Therefore, an intelligent optimisation algorithm is proposed in this paper and used for the elastic parameter inversion of pre-stack seismic data. This algorithm improves the population initialisation strategy by using the Gardner formula and the genetic operation of the algorithm, and the improved algorithm obtains better inversion results when carrying out a model test with logging data. All of the elastic parameters obtained by inversion and the logging curve of theoretical model are fitted well, which effectively improves the inversion precision of the density. This algorithm was implemented with a MapReduce model to solve the seismic big data inversion problem. The experimental results show that the parallel model can effectively reduce the running time of the algorithm.
•An intelligent optimisation algorithm is proposed for the elastic parameter inversion problem.•The improvements are the population initialization strategy and the genetic operation.•This algorithm was implemented with a MapReduce model.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The real-time location of pollution sources is the process of inverting pollution sources based on the dynamic optimization model constructed by the time-varying pollution concentration detected by ...the water quality sensor. Due to the vast quantities of the water supply networks, the water quality sensors will only be placed on critical nodes, resulting in multiple solutions. However, the increased monitoring data enhances the uniqueness of the solution. Combined with the real-time location of pollution sources, this work proposed a multi-strategy dynamic multi-mode optimization algorithm based on domain knowledge, which could guide the population search and avoid trapped into local optimal. The merging mechanism was used to keep the diversity of the population and prevent sub-population clustering on the same optimal solution. The simulation results showed that the algorithm could effectively solve the real-time location problem of pollution sources in different pipe networks and pollution scenarios.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
To improve the filtration efficiency of submicron dust by dual-layer granular bed filters, filtration experiments for micro-silica powder were conducted for removing particles smaller than 1 μm that ...account for more than 96% (by volume) using a dual-layer granular bed filter with an external electric field. Electrostatic enhancement methods, including dust pre-charging, application of an electric field to the lower filter layer, and that to both the upper and lower filter layers, were examined. Results showed that the average filtration efficiency of a dual-layer granular bed filter for micro-silica powder without electric field was 76.52%, the average outlet dust concentration was 263.53 mg/m
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, and the filtration cycle time was 73 min. With pre-charged dust, the average outlet dust concentration dropped to 82.51 mg/m
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. A decrease in the thickness of the lower filter layer from 45 to 25 mm with electric field reduced the pressure drop from 2570 to 1770 Pa. Meanwhile, the application of an electric field to the lower/upper filter layer reduced the average outlet dust concentration to 25.98 mg/m
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. Increasing the initial face velocity from 0.25 to 0.45 m/s increased the average outlet dust concentration from 25.98 to 30.27 mg/m
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and increased the pressure drop from 2570 to 3500 Pa.
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In recent years, water contamination incidents have happened frequently, causing serious losses and impacts on society. Therefore, how to quickly respond to emergency pollution incidents is a ...widespread concern for academic and industry scientists. In this paper, aiming to deal with the uncertain environment and randomness of water demand, we present a method based on a deep reinforcement learning emergency scheduling algorithm combined with Lamarckian local search. This method can effectively schedule water valves and fire hydrants to isolate contaminated water and reduce the residual concentration of contaminants in water distribution networks (WDNs). Furthermore, two optimization objectives are formulated, and then multi-objective optimization and deep reinforcement learning are combined to solve this problem. A real-world WDN is employed and simulation results show that our proposed algorithm can effectively isolate contamination and reduce the risk exclosure to customers.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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