The purpose of this paper is to perform a systematic review on the application of different multi-criteria decision-making (MCDM) methods in solving the site selection problem across multiple problem domains. The domains are energy generation, logistics, public services and retail facilities. This study aims to answer the following research questions: Which evaluating criteria were used for each site selection problem domain? Which MCDM methods were frequently applied in a particular site selection problem domain?,The goals of the systematic review were to identify the evaluating criteria as well as the MCDM method used for each problem domain. A total of 81 recent papers (2014–2018) including 32 papers published in conference proceedings and 49 journal articles from various databases including IEEE Xplore, PubMed, Springer, Taylor and Francis as well as ScienceDirect were evaluated.,This study has shown that site selection for energy generation facilities is the most active site selection problem domain, and that the analytic hierarchy process (AHP) method is the most commonly used MCDM method for site selection. For energy generation, the criteria which were most used were geographical elements, land use, cost and environmental impact. For logistics, frequently used criteria were geographical elements and distance, while for public services population density, supply and demand, geographical layout and cost were the criteria most used. Criteria useful for retail facilities were the size (space) of the store, demographics of the site, the site characteristics and rental of the site (cost).,This study is limited to reviewing papers which were published in the years 2014–2018 only, and only covers the domains of energy generation, logistics, public services and retail facilities.,MCDM is a viable tool to be used for solving the site selection problem across the domains of energy generation, logistics, public services and retail facilities. The usage of MCDM continues to be relevant as a complement to machine learning, even as data originating from embedded IoT devices in built environments becomes increasingly Big Data like.,Previous systematic review studies for MDCM and built environments have either focused on studying the MCDM techniques itself, or have focused on the application of MCDM for site selection in a single problem domain. In this study, a critical review of MCDM techniques used for site selection as well as the critical criteria used during the MCDM process of site selection was performed on four different built environment domains.

A systematic review of the applications of multi-criteria decision-making methods in site selection problems

In this paper, a multi-criteria set-covering methodology is proposed to select suitable locations for a set of data centers. First, a framework of criteria, with social, economic and environmental ...

/pdf/sustainable-location-selection-of-data-centers-developing-a-3i0x3ngov9.pdf

Sustainable Location Selection of Data Centers : Developing a Multi-Criteria Set-Covering Decision-Making Methodology

Learning and identifying key concepts from past fault records are essential for us to understand the causes of these faults, which lay the foundation for the fault diagnosis and prognosis. At present, faults in many fields (e.g., rail, automobile, and smart grid) are recorded in textual form. Due to the lack of effective mining and analysis tools, latent information in the massive textual data (text records) has not been fully unearthed. In this paper, a novel Adversarial Training-based Lattice LSTM model called AT-Lattice is proposed to address this problem. In this model, the Named Entity Recognition (NER) is achieved by Lattice LSTM and Conditional Random Field (CRF), where the Lattice LSTM is used to provide sequence information between words, and the CRF is used to get the final entity prediction result. In addition, the Chinese Word Segmentation (CWS) task is introduced to conduct the adversarial training with the NER task. The framework of the adversarial training is able to make full use of the boundary information and filter out the noise caused by the introduced CWS task. More importantly, extensive experiments are conducted on five different train fault datasets collected by a rail transit company. The results demonstrate that the proposed model outperforms the state-of-the-art baselines. 

Adversarial Training Lattice LSTM for Named Entity Recognition of Rail Fault Texts

Soil organic matter (SOM) is a key factor for evaluating soil fertility. Rapidly monitoring organic matter content in desert soil can provide a scientific basis for the rational development and utilization of reserve arable land resources. Although spectral inversion accuracy for SOM under laboratory-controlled conditions is high, it is time-consuming and costly compared to the in situ spectroscopic determination method. However, in situ spectroscopy causes losses in accuracy due to interference from external environmental factors (e.g., the surface roughness of soil, changes in weather conditions, atmospheric water vapor, etc.). Therefore, reducing or removing the interference of external environmental factors to improve the accuracy of in situ spectroscopy for estimating SOM is challenging. In this study, visible and near-infrared (Vis-NIR) in situ spectral data were collected from 135 topsoil (0–20 cm) samples in a desert area of northwestern China, and organic matter content was measured. Three spectral pre-processing methods—the standard normal transform (SNV), reciprocal logarithm (log(1/R)) and normalization (NOR)—combined with three feature variable selection methods—the particle swarm algorithm (PSO), ant colony algorithm (ACO) and simulated annealing (SA) algorithm—were used to filter the spectral feature bands of SOM, and then partial least squares regression (PLSR), a back propagation neural network (BPNN) and a convolutional neural network (CNN) were used to construct the estimation models of SOM. The results indicated that the SNV could enhance the spectral information related to SOM and improve the accuracy of model estimation, and it was one of the most effective spectral pretreatment methods. Compared with the model constructed with the full-band spectroscopy method, the feature variable selection method could effectively improve the estimation accuracy of the Vis-NIR in situ spectroscopy model. The most obvious improvement was found with PSO, where R2 and RPD were improved by more than 0.34 and 0.16, respectively, and RMSE was reduced by more than 0.29 g kg−1. The accuracy of the CNN model was higher than that of the BPNN and PLSR models, both for the inversion model of SOM built from full-band spectral data and the bands selected by the characteristic variable selection method. SNV-PSO-CNN is the optimal hybrid model for in situ spectral measurement of SOM (R2 = 0.71, RPD = 1.88, RMSE = 1.67 g kg−1) and can realize the quantitative in situ spectral inversion of SOM in desert soils.

/pdf/estimating-soil-organic-matter-content-in-desert-areas-using-1qymsuxl.pdf

Estimating Soil Organic Matter Content in Desert Areas Using In Situ Hyperspectral Data and Feature Variable Selection Algorithms in Southern Xinjiang, China

The optimal fractional system identification is a challenging problem as it requires the estimation of not only the numerator parameters, but also the poles transfer function model and the non-integer order reading to complex nonlinear optimization. In this paper, an algorithm using the least square method, called "Vector Fitting" (V.F.) developed by Gustavsen is extended to the fractional order system identification in frequency domain. This algorithm proceeds recursively contrarily to the well known Levy's algorithm which uses only one iteration to calculate the model parameters. The use of an iterative method efficiently directs the model parameters evolution towards their optimal values. Indeed, during iteration the poles of the model are calculated and used as starting poles for the next one, the stability of the identified model can thus be imposed. The V.F. algorithm is then associated with the heuristic optimization method: particle swarm optimization (PSO), leading to a new fractional system identification algorithm. The algorithm works in a hierarchical way; in a higher level, PSO determines the non-integer order and in a lower stage, the V.F. algorithm identifies the other parameters.

Vector Fitting fractional system identification using particle swarm optimization

The continuous-discrete PSO algorithm for shape formation problem of multiple agents in two and three dimensional space

In this paper, we present a neural architecture for recognizing named entity in text railway accident and fault analysis report, this neural architecture incorporates word representation and Conditional Random Field (CRF) into Bidirection Long Short-Term Memory (BiLSTM) neural network. We used Google's latest open source TensorFlow software platform to build this BiLSTM-CRF deep learning model. Based on the experiment analysis of railway accident and fault text data from 2016 to 2017, the result shows that the model has a significant improvement in recognizing named entity accuracy and recall rate, then we applyed the training model to our engineering application system to automatic extraction entity. The practice proves that the model has good applicability in theory and practice, meanwhile this experiment lay the foundation for much more text analysis in railway field.

BiLSTM-CRF Model for Named Entity Recognition in Railway Accident and Fault Analysis Report

This paper mainly discusses and analyzes the optimal number of China railway data centers and the corresponding positions considering the distance factor and the data's volume of each railway bureau. Firstly, optimal site selection problem on single data center is mainly solved by the Lagrangian multiplier method. Secondly, optimal site selection problem on several data centers is mainly overcome by the PSO algorithm. Eventually, numerical results in the paper highlight that the single data center mainly locates into Zheng Zhou railway bureau and two data centers locate into Bei Jing railway bureau and Cheng Du railway bureau. Additionally, three data centers locate into Bei Jing railway bureau, Wu Han railway bureau and Cheng Du railway bureau, while four data centers locate into Bei Jing railway bureau, Wu Han railway bureau, Cheng Du railway bureau and Guang Zhou railway bureau.

Optimal site selection of China railway data centers by the PSO algorithm

High-speed railway catenaries are vital components in railway traction power supply systems. To ensure stable contact between the pantograph and the catenary, droppers are positioned between the messenger wire and contact line. The failure of one or more droppers will affect the power supply of the catenary and the operation of the railway. In this paper, we modify the You Only Look Once version five (YOLOv5) model in several ways and propose a method for improving the identification of dropper status and the detection of small defects. Firstly, to focus on small target features, the selective kernel attention module is added to the backbone. Secondly, the feature graphs of different scales extracted from the backbone network are fed into the bidirectional feature pyramid network for multiscale feature fusion. Thirdly, the YOLO head is replaced by a decoupled head to improve the convergence speed and detection accuracy of the model. The experimental results show that the proposed model achieves a mean average precision of 92.9% on the dropper dataset, an increase of 3.8% over the results using YOLOv5s. The detection accuracy of small dropper defects reaches 79.2%, representing an increase of 10.8% compared with YOLOv5s and demonstrating that our model is better at detecting small defects.

/pdf/yolov5s-d-a-railway-catenary-dropper-state-identification-3mkz9xck.pdf

YOLOv5s-D: A Railway Catenary Dropper State Identification and Small Defect Detection Model

Considering the safety factors in the railway, this paper mainly focuses on the optimal speed of high-speed train or locomotive from the starting station to the ending station. Firstly, some concepts in the railway safety, which include the risk source, hidden danger, accident and safety warning point, are introduced to better understand the railway safety factors. Secondly, the mathematical model including objective function and the corresponding constraints is analyzed to solve the optimal train speed problem. Thirdly, the PSO algorithm is used to provide and design the optimal speed train from the starting station to the ending station. Simulation results show that PSO algorithm can handle with the optimal train speed problem and the obtained optimal speed are provided to ensure the train safety in the constant transportation time.

Optimal Train Speed Optimization under Several Safety Points by the PSO Algorithm

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