Limin Jia | Beijing JiaoTong University (original) (raw)

Papers by Limin Jia

The subway station is an important place for passenger flow distribution in subway networks, and ... more The subway station is an important place for passenger flow distribution in subway networks, and real-time monitoring of passenger flow within stations helps promote the safe and efficient operation of the entire subway network. However, large-scale crowded passenger flow still exist in subway stations, making it challenging to accurately assess the massive passenger flow inside the stations. In this regard, this paper proposes a crowd counting method based on a deep learning framework with a dual attention mechanism. It aims to tackle the problem of counting large crowds within a subway station. This method provides strong support for ensuring passenger safety in subsequent operations. The key components of our proposed model are the multi-scale attention module and the deformable attention module. The multi-scale attention module can effectively extract multi-scale features of crowds and extract informative features from heavily occluded areas while ensuring that the channel count and resolution of the input feature map remain unchanged. In the Transformer framework, the deformable attention module dynamically assigns attention weights to each feature position, enabling a more suitable crowd counting model for congested conditions in subway station. Three commonly used benchmark datasets for crowd counting were used in a wide range of experiments. The experimental results demonstrate that our model achieves relatively good performance compared to existing popular algorithms. Additionally, existing crowd counting datasets do not adequately capture the variations in multi-scale and crowd occlusion scenarios specific to subway station environments. Therefore, this paper constructed a custom dense crowd dataset for subway station platforms. Our method performs better on this self-built dataset, which focuses on capturing the challenges of multi-scale variations and crowd occlusions in subway station scenarios, as demonstrated by experimental results.

IEEE Access, 2019

The health condition of a wheelset bearing, the key component of a railway bogie, has a considera... more The health condition of a wheelset bearing, the key component of a railway bogie, has a considerable impact on the safety of a train. Traditional bearing fault diagnosis techniques generally extract signals manually and then diagnose the bearing health conditions through the classifier. However, high-speed trains (HSTs) are usually faced with variable loads, variable speeds, and strong environmental noise, which pose a huge challenge to the application of the traditional bearing fault diagnosis methods in wheelset bearing fault diagnosis. Therefore, this paper proposes a 1D residual block, and based on the block, a novel deeper 1D convolutional neural network (Der-1DCNN) is proposed. The framework includes the idea of residual learning and can effectively learn high-level and abstract features while effectively alleviating the problem of training difficulty and the performance degradation of a deeper network. Additionally, for the first time, we fully use the wide convolution kernel and dropout technology to improve the model's ability to learn low-frequency signal features related to the fault components and to enhance the network's generalization performance. By constructing a deep residual learning network, Der-1DCNN can adaptively learn the deep fault features of the original vibration signal. This method not only achieves very high diagnostic accuracy for the fault diagnosis task of wheelset bearings in HSTs under strong noise environment, but also its performance is quite superior when the train's working load changes without any domain adaptation algorithm processing. The proposed Der-1DCNN is evaluated on the dataset of the multi-operating conditions of the wheelset bearings of HSTs. Experiments show that this method shows a better diagnostic performance compared with the state-of-the-art deep learning methods of bearing fault diagnosis, which proves the method's effectiveness and superiority. INDEX TERMS High-speed trains, wheelset bearings fault diagnosis, deep learning, one-dimensional residual block, wide convolutional kernel.

Sustainability, Oct 28, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Discrete Dynamics in Nature and Society, 2017

In view of the negative impact of component importance measures based on system reliability theor... more In view of the negative impact of component importance measures based on system reliability theory and centrality measures based on complex networks theory, there is an attempt to provide improved centrality measures (ICMs) construction method with fuzzy integral for measuring the importance of components in electromechanical systems in this paper. ICMs are the meaningful extension of centrality measures and component importance measures, which consider influences on function and topology between components to increase importance measures usefulness. Our work makes two important contributions. First, we propose a novel integration method of component importance measures to define ICMs based on Choquet integral. Second, a meaningful fuzzy integral is first brought into the construction comprehensive measure by fusion multi-ICMs and then identification of important components which could give consideration to the function of components and topological structure of the whole system. In addition, the construction method of ICMs and comprehensive measure by integration multi-CIMs based on fuzzy integral are illustrated with a holistic topological network of bogie system that consists of 35 components.

Multimedia Tools and Applications, Aug 30, 2022

COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail tra... more COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail transit stations. However, the current face detection algorithms cannot distinguish between a face wearing a mask and a face not wearing a mask. This paper proposes a face detection algorithm based on single shot detector and active learning in rail transit surveillance, effectively detecting faces and faces wearing masks. Firstly, we propose a real-time face detection algorithm based on single shot detector, which improves the accuracy by optimizing backbone network, feature pyramid network, spatial attention module, and loss function. Subsequently, this paper proposes a semi-supervised active learning method to select valuable samples from video surveillance of rail transit to retrain the face detection algorithm, which improves the generalization of the algorithm in rail transit and reduces the time to label samples. Extensive experimental results demonstrate that the proposed method achieves significant performance over the state-of-the-art algorithms on rail transit dataset. The proposed algorithm has a wide range of applications in rail transit stations, including passenger flow statistics, epidemiological analysis, and reminders of passenger who do not wear masks. Simultaneously, our algorithm does not collect and store face information of passengers, which effectively protects the privacy of passengers.

Sensors

The state monitoring of the railway track line is one of the important tasks to ensure the safety... more The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, ...

Smart and Resilient Transportation

Purpose This paper aims to define the concept, composition, connotation, functional technology an... more Purpose This paper aims to define the concept, composition, connotation, functional technology and development path of autonomous transportation systems (ATS) and provide theoretical basis and support for the construction and development of ATS. Design/methodology/approach The research analyzes the concept and connotation of ATS, studies the composition and structure of ATS, sorts out pillar function technology system including perception, digitization, interoperability, computing and integration in ATS hierarchically, and looks forward to the future development path of ATS from human participation and systems intelligence. Findings This paper puts forward the concept, composition, connotation and structure of ATS, proposes the pillar functional technology system of ATS and proposes four development stages of ATS. Originality/value The research can provide a theoretical and scientific basis for the high-quality, efficient, orderly construction and development of ATS.

Sustainability, Nov 24, 2020

With the increase of the railway operating mileage, the railway network is becoming more and more... more With the increase of the railway operating mileage, the railway network is becoming more and more complicated. We expect to build more railway lines to offer the possibility to offer more high quality service for the passengers, while the investment is often limited. Therefore, it is very important to decide the pairs of cities to add new railway lines under the condition of limited construction investment in order to optimize the railway line network to maximize the reliability of the railway network to deal with the railway passenger transport task under emergency conditions. In this paper, we firstly define the reliability of the railway networks based on probability theory by analyzing three minor cases. Then we construct a reliability optimization model for the railway network to solve the problem, expecting to enhance the railway network with the limited investment. The goal is to make an optimal decision when choosing where to add new railway lines to maximize the reliability of the whole railway network, taking the construction investment as the main constraint, which is turned to the building mileage limit. A computing case is presented based on the railway network of Shandong Province, China. The computing results prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the railway investors and builders to make an optimal decision.

Lecture notes in electrical engineering, 2020

Accurate estimated capacity of the railway section can provide reliable information to railway op... more Accurate estimated capacity of the railway section can provide reliable information to railway operators and engineers in decision-making, particularly, in an emergency situation. However, in an emergency, the optimization of capacity of a railway section is usually involved to study, for example, the characteristics of dynamic, fuzziness, randomness, and non-aftereffect properties. This paper presents a proposed capacity calculation method based on the modified fuzzy Markov chain (MFMC). In this method, the capacity of a railway section in an emergency can be expressed by a fuzzy random variable, which remains the randomness of capacity changing according to the impact of emergencies and the fuzziness of the driving behavior and other factors. A case study of a high-speed line from Beijing to Shanghai is used to show the process of the proposed methods for optimization of section capacity calculation in an emergency.

Journal of Advanced Transportation

In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green a... more In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green and sustainable, the optimization and development of public transportation, as the subject of people's long-term research, has always been shining. With the emergence of shared transportation, public transportation systems face more challenges. In order to better connect with bike-sharing, car-sharing, and other modes of transportation, public transportation will carry out important reforms, among which the optimization of line network is one of the most important tasks. The traditional bus route design is mainly based on the “four-stage” method model, which is mainly based on the investigation and analysis of the existing traffic system and land use. Through the work flow of “evaluation, calibration, and verification,” the network balance optimization model is used to get the bus travel allocation prediction model. In this paper, the optimization problem of public transit network is ...

Smart and Resilient Transportation

Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway e... more Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO2 emission reduction of highways. Design/methodology/approach To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO2 emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility. Findings The results show that through the appl...

China's High-Speed Rail Technology, 2022

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Travel Demands influence micro-level traffic behavior, furthermore traffic states. In o... more Travel Demands influence micro-level traffic behavior, furthermore traffic states. In order to evaluate the effect of travel demands on traffic states, this paper introduces the Demand- Motivation-Behaviors (DMB) micro traffic behavior analysis model which denotes that vehicles behaviors are determines by motivations that relies on traffic demands from the perspective of behavior science. For vehicles, there are two kinds of travel demands: reaching travel destinations from orientations and meeting expectations of travel speed. To satisfy travel demands, the micro traffic behaviors are delivered such as car following behavior, optional and mandatory lane changing behaviors. Especially, mandatory lane changing behaviors depending on travel demands take strong impact on traffic states. In this paper, we define the DMB-based cellular automate traffic simulat...

Sustainability, 2021

To promote the development level of urban sustainability, more and more cities have been paying a... more To promote the development level of urban sustainability, more and more cities have been paying attention to the improvement of public transportation. City managers intend to attract people from private cars to public transport by improving the service level of urban public transport. The operational reliability of bus lines plays a crucial role in maintaining high-level service of public transportation. Previous studies have focused on the service level of the whole line by investigating the overall stability of departure punctuality, line running time, and punctuality. This study aims to clarify the relationship between the whole line and sites in terms of the influence on the operational reliability of bus lines. This study proposes a bus route reliability evaluation method, based on copulas connect function, and the actual line data are taken as a case study. The results show that the method reveals the relationship between the overall line and the interstation paths in the cont...

Tsinghua Science and Technology, 2022

Vehicle information on high-speed trains can not only determine whether the various parts of the ... more Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally, but also predict the train's future operating status. How to obtain valuable information from massive vehicle data is a difficult point. First, we divide the vehicle data of a high-speed train into 13 subsystem datasets, according to the functions of the collection components. Then, according to the gray theory and the Granger causality test, we propose the Gray-Granger Causality (GGC) model, which can construct a vehicle information network on the basis of the correlation between the collection components. By using the complex network theory to mine vehicle information and its subsystem networks, we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network. In addition, the vehicle information network is weak against attacks, but the subsystem network is closely connected and strong against attacks.

Journal of Engineering Science and Technology Review, 2013

The working state of rolling bearing has an important influence on the operation of trains, direc... more The working state of rolling bearing has an important influence on the operation of trains, directly related to the safety of train passengers. Therefore, it has great significance to conduct train bearing fault diagnosis. In this paper, based on harmonic wavelet envelope, a method for the fault diagnosis of railway bearings is proposed. First of all, the harmonic wavelet packet was used to translate vibration signal into timescale representation. Then the decomposed signal was demodulated. Finally, through the analysis of the envelope spectrum, the bearing fault feature frequency was extracted. In order to verify the validity of diagnosis method, outer race fault bearing and ball fault bearing were tested. The test results show that the diagnosis method is effective and practical.

Nano Energy, 2017

Vibration exists everywhere especially in the public railway operation system. The vibration acce... more Vibration exists everywhere especially in the public railway operation system. The vibration acceleration is the key factor to monitor and evaluate the structure health of the railway equipment. In this paper, a kind of self-powered triboelectric nano vibration accelerometer (TEVA) is presented. A low frequency spring mass vibration model is built to calculate the vibration sensitive performance and the electric output of the TEVA. The prototype of the TEVA is demonstrated and characterized through the railway vibration simulation platform. It has been testified that TEVA can successfully harvest the low frequency vibration energy and convert it to electrical power to achieve the self-powered vibration acceleration monitoring system. The output current and voltage of TEVA are also sensitive to the vibration acceleration from 1.07m/s 2 to 1.25m/s 2 linearly. Hence it can be used as a self-powered nano vibration accelerator for the fault diagnosis. In addition, the generated electricity is used for charging the lithium battery (from 1.5V to 3.1V) which supplies power to the ZigBee module. The experiment shows that the charged battery through TEVA can support the wireless communication between ZigBee modules, with temperature and humidity sensors embedded on it. The temperature and humidity on the train are 22 degree Celsius and 35%RH respectively. Therefore, the vibration energy can be harvested and stored for the power supply of wireless sensor network nodes in the near future. Keyword: Self-powered vibration accelerometer, Spring Mass Model, triboelectric nanogenerator, wireless sensor, state health monitoring 1.Introduction With the development of the high speed railway, the safety operation of railway system has attracted more and more attentions. Key equipment of railway system such as bogies and railway tracks need to be inspected to ensure the safety and reliability during the operation [1]. Information such as using life span and fault classifications derived from the inspection is pretty important for the railway operation safety. Therefore, the state health monitoring (SHM) of key equipment is very necessary. There are a lot of methods for Railway SHM (RSHM), including temperature monitoring, acoustic monitoring and vibration signal monitoring. For example, the wheel brake temperature monitoring will supply feedback to the train driver [2]. And the air temperature and humidity monitoring of the train carriage will prevent the breakdown of traction power system. Vibration signal analysis has many advantages and suitable for almost every kind of railway key equipment. Besides, the collected vibration signal is easily stored and to process. The processing method is various and the fault diagnosis result is accurate [3-6]. The vibration signal analysis method has been used widely, a lot of researches has applied this method for the railway key

The Open Cybernetics & Systemics Journal, 2015

In practice, due to the lack of information, imprecise variables which come from experts' empiric... more In practice, due to the lack of information, imprecise variables which come from experts' empirical data usually appear. In order to deal with these imprecise variables, uncertainty theory is proposed and has been proved to be an efficient method. This paper introduces uncertainty theory into travelling salesman problem (TSP), in which the link travel times are assumed to be uncertain variables, and then a chance constrained programming model is proposed within the framework of uncertainty theory. The properties of the chance constrained programming model are investigated; furthermore, the uncertain model is proved to be equivalent to a deterministic model. To solve the problem, we design an algorithm based on genetic algorithm. Finally, a numerical example is given, the result of which verifies the effectiveness of the proposed chance constrained programming model and the algorithm.

Journal of Engineering Science and Technology Review, 2013

In this paper we present a novel method in fault recognition and classification in urban rail veh... more In this paper we present a novel method in fault recognition and classification in urban rail vehicle auxiliary inverter based on wavelet packet and Elman neural network. First, the original fault voltage signals are decomposed by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are used as Elman neural network input parameters to realize intelligent fault diagnosis. The result shows that the Elman neural network is better than BP neural network, it is effective to distinguish the state of the urban rail vehicle auxiliary inverter.

Smart and Resilient Transportation, 2020

Purpose This paper aims to investigate the reliability, availability, maintenance and safety anal... more Purpose This paper aims to investigate the reliability, availability, maintenance and safety analysis method for railway network operation. Design/methodology/approach The reliability of the railway network is proposed based on the accident frequency and the topology of the railway network. Network efficiency and capacity are proposed to evaluate the availability of the railway network. The maintenance of the railway network is analyzed from the perspective of accident recovery time. The safety index of the railway network is proposed to measure the safety of railway stations and sections and the K-means method is proposed to find the safety critical stations and sections. Finally, the effectiveness of the proposed method is illustrated through a real-world case study. Findings The case study shows that the proposed model can produce a big-picture averaged view of the network-wide safety level and help us identify the safety critical stations and sections by considering both the exp...

IEEE Access, 2019

Sustainability, Oct 28, 2021

Discrete Dynamics in Nature and Society, 2017

Multimedia Tools and Applications, Aug 30, 2022

Sensors

Smart and Resilient Transportation

Sustainability, Nov 24, 2020

Lecture notes in electrical engineering, 2020

Journal of Advanced Transportation

Smart and Resilient Transportation

China's High-Speed Rail Technology, 2022

Sustainability, 2021

Tsinghua Science and Technology, 2022

Journal of Engineering Science and Technology Review, 2013

Nano Energy, 2017

The Open Cybernetics & Systemics Journal, 2015

Journal of Engineering Science and Technology Review, 2013

Smart and Resilient Transportation, 2020