Research on path guidance of logistics transport vehicle based on image recognition and image processing in port area

1. RESEARCH Open Access
Research on path guidance of logistics
transport vehicle based on image
recognition and image processing in port
area
Liupeng Jiang*
, Yongjiao Fan, Qianying Sheng, Xuejun Feng and Wei Wang
Abstract
Due to the messy logistics goods in the port area, some automatic transport vehicles often have errors in cargo
transportation due to large path identification errors. Based on this, this study is based on image recognition
technology, taking the most common logistics transport vehicles in the port area as the research object and using
video image recognition technology as a guiding technology to perform image recognition processing on the
ground guidance path. Simultaneously, this study determined the image preprocessing method which is more
favorable for visual navigation, used the morphological knowledge of the image to detect the edge of the path
image, then determined the position of the path center line, and carried out simulation analysis. The research shows
that the results of this study have certain practicality and can provide theoretical references for subsequent
related research.
Keywords: Port area, Image recognition, Image processing, Logistics transport vehicle
1 Introduction
Due to the large number of goods in the port area, the
logistics path is complex, and the climate inside the port
area is relatively humid and there are many water va-
pors, the logistics path will be seriously affected, and the
logistics and transportation will often be confused. Based
on this, it is necessary to use a reasonable transportation
method to effectively guide the logistics transport vehi-
cles. In the information age, the transportation of port
area mostly uses automatic transport vehicles as the
main transport vehicles. Therefore, this study takes the
automatic transport vehicles as an example to conduct
transport route guidance research.
In the late 1950s, automatic transport vehicles began
to be used in warehouse automation and factory opera-
tions, and Japan began to introduce automatic transport
vehicle technology from then on. In the mid-1970s, due
to the application of integrated circuit technology and
electronic technology in automatic transport vehicles,
the automatic transport vehicles were significantly im-
proved in terms of automation and control performance.
The automatic transport vehicle entered the production
system as a production component and has also been
rapidly developed [1]. During this period, European
companies standardized the size and structure of pallets
for containers, which accelerated the development of au-
tomated transport technology in Europe [2]. The first
International Conference on Unmanned Pallets was held
in June 1981 in London, England, which has shown a
breakthrough in the technology of automatic transport
vehicles [3]. In 1984, General Motors first tried an auto-
mated transport vehicle on their flexible assembly sys-
tem. Two years later, the number of automatic transport
vehicles increased to 1407, making it the world’s largest
automatic transport vehicle user [4]. In 1985, due to the
development of computer microprocessors and the rise
of control technology, computer communication and
identification technology entered the field of automatic
transport vehicles, and automatic transport vehicles de-
veloped toward intelligence [5]. In 1987, it was reported
that Sweden first used automatic transport vehicles in
* Correspondence: jsjlp@hhu.edu.cn
College of Harbour, Coastal and Offshore Engineering, Hohai University,
Nanjing, China
EURASIP Journal on Image
and Video Processing
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Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141
https://doi.org/10.1186/s13640-018-0384-5

2. the field of logistics systems in 1969 [6]. In 1974, the
Swedish Volvo Car Company applied automatic trans-
port vehicles to their car assembly lines. This improve-
ment has been a huge success, not only reducing labor,
assembly time, and assembly failures, but also speeding
up capital flow by 43% [7]. Many Western European
countries have followed suit because of the practical eco-
nomic benefits of this application. In the 1980s, the wave
of automatic transport vehicles in Europe flooded into
the US market. Many US companies transferred ad-
vanced automatic guided trolley technology to Metron
through technology introduction and joint ventures ([8].
In 1978, a direct computer-controlled automated trans-
port vehicle system introduced from Europe was suc-
cessfully applied at the Keebler Distribution Center in
Chicago, USA [9]. In 1981, John Corporation of the USA
applied automated transport vehicles to automated
warehouses to increase the efficiency of material hand-
ling during manufacturing, and the entire process was
monitored in real time [10]. The French outdoor guide
automatic transporter runs at speeds of up to 97 km/h
[11]. In the following 10 years, various intelligent auto-
matic transport vehicles have emerged in an endless
stream. The automatic transport vehicles based on visual
navigation have also been greatly developed. At this
time, the typical visual navigation system mainly in-
cludes ARCADE system of the University of Michigan;
LANA system [12]; NAVLAB system of Carnegie Mellon
University, USA; EMS, Vision system of German Federal
University of Defense; ROMA system of IITB of
Germany; and PVS system of MITI of Japan. According
to statistics, by 2004, there were more than 16,000 auto-
matic transport vehicle systems in use worldwide and
more than 100,000 automatic transport vehicles [13].
In China, the development of automatic transport ve-
hicles started late. In 1975, China’s first electromagnetic-
ally guided three-wheeled automatic transport vehicle
was successfully developed at the Beijing Crane Trans-
portation Machinery Research Institute [14]. In the late
1980s, the Beijing Machinery Industry Automation Re-
search Institute developed the first automatic transport
vehicle that can be used in the three-dimensional ware-
house and successfully applied in the Second Automo-
bile Group. During the same period, the Beijing Postal
Research Institute also successfully developed the Auto-
mated Guided Car IV [15] using two-way wireless com-
munication technology. In the 1990s, China’s research
on automatic transport vehicles entered a period of rapid
development. The automatic transport vehicle imported
from abroad was successfully applied to the CIMS ex-
perimental research at the National CIMS Engineering
Center of Tsinghua University. Shenyang Institute of
Automation developed an automatic transport vehicle
for automobile engine assembly for Jinbei Automobile
Co., Ltd., which is a successful application in China’s
automobile industry [16]. Kunming Marine Equipment
Research Institute successfully developed an automatic
transport vehicle based on laser/infrared guidance for
the first time. The Intelligent Vehicles Group of Jilin
University is mainly engaged in the research of auto-
matic transport vehicles based on visual navigation. It
has developed JUTIV1 and JUTIV2 visual navigation ve-
hicles. On this basis, they developed the JUTIV3,
general-purpose and assembly-type visual navigation
automatic transport vehicles for the actual application of
the factory [17].
In the control algorithm of the automatic transport ve-
hicle, the most widely used is PID control, because the
adjustment parameters are convenient. With the devel-
opment of intelligent control technology, many auto-
matic transport vehicles using fuzzy control have
emerged. In addition, advanced control algorithms such
as expert systems, neural networks, learning, and genetic
algorithms have also seen a lot of research, but at
present, they only exist in the theoretical research stage,
and there are few application examples in real engineer-
ing. In order to study the visual navigation automatic
transport vehicle based on image processing, this paper
conducts a guided analysis of the logistics transportation
path in the port area.
2 Research methods
As shown in Fig. 1, the automatic transport vehicle in-
volved in this paper is a two-wheel differential adjust-
ment, which includes two drive wheels and one driven
wheel. In order to analyze the problem conveniently, the
driven wheel is not considered, and the friction between
the wheel and the ground is not considered, and only
the two driving wheels are analyzed. Because the
wheeled automatic transport vehicle has the advantages
of easy control, easy sliding friction, stable motion, no
need to consider the balance problem during driving,
and low energy consumption, it has become one of the
Fig. 1 Schematic diagram of automatic transport vehicle navigation
Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141 Page 2 of 8

3. favorite fields of intelligent service robots. Therefore, the
kinematics analysis of the wheeled automatic transport
vehicle and the establishment of mathematical models
have brought together the results of many researchers.
The automatic transport vehicle of Harbin Institute of
Technology’s Bitong Intelligent Robot Research Center
is used in automobile production assembly workshops.
It adopts three-wheel mode, the front two-wheel differ-
ential adjustment is the driving wheel, and the back is a
universal wheel, which plays a supporting role and does
not play a major role in the adjustment of speed and dir-
ection. Figure 1 is a schematic diagram of the navigation
of the automatic transport vehicle. In Fig. 1, the driving
wheel is on the left and right sides of the front, the
broken line is the navigation track, Vt, Vr is the linear
velocity of the left and right wheels, ω is the angular vel-
ocity of the two wheels, and O is the center point of the
two-wheel spacing.
Since the rear universal wheel does not participate
in adjusting the speed and direction, we have further
simplified the model in the kinematics analysis, using
only two wheels instead of the automatic transport ve-
hicle. The simplified model diagram is obtained as
shown in Fig. 2.
Using only two wheels instead of the automatic trans-
port vehicle, we assume that the car body quality is
even, the center position C of the two wheels is the
center of gravity of the car body, the speed of the cen-
ter point of the car body is Vn, the diameter of the
wheel is D, and O1 and O2 are the centers of the left
and right wheels respectively. At the same time, we as-
sume that L is the distance between the center of the
left and right wheels, O is the center of the space rota-
tion of the automatic transport vehicle, and the
distance between O and C is the radius of rotation,
denoted as R. The relationship between the center
speed of the vehicle body and the speed of the left and
right wheels can be obtained as follows.
Vn ¼ Vr þ Vlð Þ=2
ω ¼ Vr−Vlð Þ=D
ð1Þ
The above equation can be transformed to obtain a re-
lational Expression (3) between the radius of rotation R
and the speed of the vehicle body Vl, Vr.
2π R−
D
2
Vl
¼
2π R þ
D
2
Vr
ð2Þ
R ¼
D
2
Vr þ Vl
Vr−Vl
ð3Þ
Assuming that the navigation speed V_n is fixed, we
can substitute the formula (1) into the Eq. (3), and then
obtain the simplified formula (4). Normally, the auto-
matic transport vehicle travels at a stable navigation
speed V. Therefore, different driving rules can be clearly
derived from Eq. (4) to draw the following conclusions.
R ¼
DVn
Vr−Vl
ð4Þ
When Vr = Vl, R = ∞, Vn = Vr = Vl, which indicates that
the automatic transport vehicle performs linear transla-
tion motion at this time. When Vr ≠ Vl, Rϵ(0, ∞), which
indicates that the automatic transport vehicle performs
circular motion with R as the radius of rotation. In short,
the camera plays a role as a projector. This transform-
ation can be represented by orthogonal transformation
or geometric perspective transformation. The model of
orthogonal transformation is a model in which the ap-
pearance of the imaging plane does not change with the
positional change of the camera in the environment, and
the geometric perspective model changes according to
the position change of the camera. In the case of auto-
matic transport vehicles, the imaging model is often rep-
resented by a geometric model. The image information
includes not only the path, but also the environment
and noise interference. The purpose of preprocessing is
to remove various noise interferences, distinguish the
environment from the path, and identify the path. After
image pre-processing, the path is basically separated
from the environment, and the path is the area between
two lines with a certain width. In order to more easily
extract the positional deviation and angular deviation of
the path, the centerline position of the path is fitted to a
straight line in this design, and its width is ignored.
Using this line as the standard of the path, it is more ac-
curate and convenient to obtain the deviation value.
Fig. 2 Simplified model diagram
Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141 Page 3 of 8

4. The image captured by the camera is preprocessed to
obtain the image as shown below. The coordinate sys-
tem is established thereon, the horizontal right direction
is the positive direction of the X-axis, the direction per-
pendicular to the X-axis is the positive direction of the
Y-axis, the white area is the path information, the line
center line equation is y = kx + b, the distance between
the line and the Y-axis is d, and the angle between the
line and the Y-axis is the angle deviation. From Fig. 3,
we can see that the path in the image exists due to the
installation position of the camera on the automatic
transport vehicle, the height from the ground, the angu-
lar difference from the horizontal position, and the dif-
ference in the distance between the camera and the
path, the width of the path in the image is inconsistent.
The farther the distance is, the narrower the width is,
which also affects the accuracy and stability of naviga-
tion to some extent. Therefore, it is desirable to use the
straight line of the center line of the path as the path.
After drawing this line, the pixel on the line is the pixel
of the path, which can also eliminate the interference of
some discrete noise and improve the denoising ability.
The main principles and processes of the path center-
line measurement algorithm are as follows: 3 × 3 square
structure elements are selected, the image is etched, and
the etched image is subtracted from the original image
to get Edge_Road. Then, a zero-matrix CenterR of the
same size as the original image is created, and a column
vector with the number of the CenterR is constructed.
The column vector is compared with the column vector
in the image from the left side. The path is all labeled 1
and the environment is all labeled as 0. Therefore, if
Edge_Road is not equal to 1, the algorithm continues to
advance to the right for comparison. When Edge_Road
is equal to 1, it indicates that the left edge position of
the path is detected, and the coordinates are located,
denoted as P1. Then, the position detection of the right
edge is performed, and the detection method is similar
to the left edge detection. The difference is that the ini-
tial detection starts from the right side of the image, and
when the pixel is detected to be 1, the detection is
stopped, that is, the right edge position of the path is re-
corded as P2. When the left and right edge positions
have been determined, then the position of the left and
right edge position coordinates and the position of one
half are the position coordinate of the path center line.
Finally, the pixel value at this coordinate is assigned a
value of 1, the other pixels are marked as 0, and the final
result is obtained.
Experiments prove that this method is effective. It
can be seen from Fig. 3 that the path of different
widths, through the method of locating the centerline,
finally integrate a straight line. This line is the pos-
ition of the center line of the path shown by the
broken line in Fig 3, which effectively facilitates the
extraction of positional deviation and angular devi-
ation. At the same time, due to taking the center pos-
ition and re-assigning, the influence of discrete noise
is suppressed to some extent. Comparing the result
graph with the graph in the previous section, it is not
difficult to find that the two white noise points below
the original path no longer exist in the processed re-
sult graph, and the expected effect is achieved.
After designing the fuzzy controller model, we need to
save the model and store the result in a matrix buffer in
the form of a matrix. If we want to make the designed
controller enter the simulation test as a module on the
Simulink simulation platform, we can achieve the pur-
pose by assigning the output matrix variable as a param-
eter to a packaged fuzzy module.
This paper chooses MATLAB to carry out simulation
test of fuzzy controller and define input and output vari-
ables. First, we entered the FIS (Fuzzy Inference System)
editor and set the name and number of the fuzzy con-
troller’s input and output variables, the blur factor, and
the clearing method through the selection of the main
menu. It can be set in order as needed. Figure 4 shows
the input and output variables selected according to the
requirements of the fuzzy controller designed in this
paper. The linguistic variable membership function is
Fig. 3 Path coordinate system Fig. 4 a, b Path center line location map
Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141 Page 4 of 8

5. then defined as described in the previous table. The
steeper the shape of the membership function, the
higher the resolution and the higher the control sensitiv-
ity. On the contrary, if the change of the membership
function is slow, the control characteristics are also
gentle. We choose the triangle membership function as
needed.
Then, the fuzzy control rule is defined. First, we enter
the main interface of the fuzzy rule editor, which is es-
sentially a text edit box. Fuzzy rules are shown in the
table. Here, we write the rules to the editor in turn ac-
cording to the fuzzy rule writing format. After the fuzzy
rule is correctly input according to the input mode of
the specified editor, the 3D preview can be accessed
through the main menu of the editor. After designing
the fuzzy controller model, we need to save the model
and store the result in a matrix buffer in the form of a
matrix. If you want to make the designed controller
enter the simulation test as a module on the Simulink
simulation platform, then we need to assign the output
matrix variable as a parameter to a packaged fuzzy mod-
ule to achieve the goal.
3 Results
In this article, the experimental platform was first built,
then a large number of test experiments were carried
out on the experimental platform, the experimental data
was carefully recorded, and the experimental data was
carefully classified. It was found that the proportion of
navigation success was more than half, and the effective-
ness of the method was tested. At the same time, we
carefully analyzed the experimental group of navigation
failures in the experiment, tried to find out the key fac-
tors affecting navigation, and reasonably forecast the fu-
ture research and development direction. The specific
results are as follows.
The linguistic variable membership function is defined.
Figure 5 shows the membership function of the control
quantity U.
The result of the 3D preview is the sharpened result
graph, and the three axes represent the two inputs and a
single output of the system. Through the 3D graph, we
can query the domain of the fuzzy variable and verify
whether the above fuzzy rules and anti-fuzzification pro-
cesses are correct. From Fig. 6, we can check the cor-
rectness and rationality of the designed controller from
multiple angles and intuitively.
A set of simulation data is given as shown. Figure 7
shows the trajectory of the automatic transport vehicle
tracking circular. The red trajectory in Fig. 7 is the set
path, and the blue trajectory is the trajectory when the
automatic transport vehicle tracks the path. The radius
of the circle is 2 m, which simulates the actual minimum
radius of rotation in the real world. The starting position
starts from the center of the circle and looks for the tra-
jectory to the right. It can be seen in Fig. 7 that the cir-
cular trajectory can be well tracked.
There are a positional deviation and an angular devi-
ation in the path of the automatic transport vehicle
transport task and the actual operation. Therefore, the
control system needs to correct the running posture in
real time, make it set the route to run smoothly, and
simulate the travel route of the automatic transport ve-
hicle when actually carrying out the transportation task.
The automatic transport vehicle starts from the center
of the circle corresponding to the simulated route.
When it reaches the far right, it starts to track the cir-
cumference and finally returns to the starting point of
the circumference, and the transport task is completed.
Automated transport vehicles follow a circular path
when performing simulation tests. Since the automatic
transporter follows the circular motion, it must be con-
stantly corrected in the X direction because the motion
trajectory is curved. We observe the error value of the
automatic transport vehicle trajectory in the X direction
through the observer. Similarly, when the automatic
transporter moves in a circular motion, it needs to be
Fig. 5 The membership function of the control quantity U Fig. 6 3D image after defuzzification
Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141 Page 5 of 8

6. constantly corrected in the Y-axis direction, so that the
trolley can travel according to the set path. Figure 8 is
the error curve in the X-axis direction, and the step size
is 1000. When the automatic transport vehicle runs nor-
mally along the trajectory, the error in the X-axis direc-
tion is about 2 cm, which is within the error tolerance.
The reason for this error is that the trajectory is circular,
and each trajectory has a curvature, so it is necessary to
constantly adjust the traveling direction to advance
along the path centerline position.
Figure 9 shows the error in the Y-axis direction. When
the automatic guided vehicle starts normal operation
along a circular path, the error value in the Y-axis direc-
tion is about 2 cm, which is acceptable in the error toler-
ance range.
After the experimental platform was built, a large
number of experiments were carried out in the indoor
trajectory and the number of experiments was more
than 1000 times. Experiments were carried out on
straight trajectories, elliptical trajectories, circular trajec-
tories, and so on. Due to the large amount of experi-
mental data, only one of the groups was listed in this
study. The data of the experiment was carried out on a
straight trajectory with a distance of 15 m. The speed
was adjustable, and the accuracy of reaching the destin-
ation was used as the evaluation standard.
4 Discussion and analysis
When the car reaches the destination from the start-
ing point, the error is recorded as qualified at about 3
cm. When the error is exceeded, it is recorded as the
navigation failure. The total number of experiments
was 200, the number of successful navigation was 178,
the number of failures was 22, the success rate was
89%, and the failure rate was 11%. The detailed ana-
lysis below causes the error to be too large to cause
navigation failure and improvement.
There are two experimental data in Table 1 that the
distance from the centerline of the path exceeds 10 cm
Fig. 7 Tracking results of a circular path
Fig. 8 Error analysis in the X direction
Fig. 9 Error analysis in the Y direction
Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141 Page 6 of 8

7. when the car arrives at the destination. In the local ex-
perimental environment at the time, this error value was
too large and it was a serious navigation failure. The rea-
son for this situation may be that the algorithm adapt-
ability is poor. When the end of a loop does not
correctly assign a value to the variable update, the navi-
gation fails and the car directly runs out of the trajectory
a long distance. In response to this problem, we can im-
prove the algorithm, for example, after initialization,
check whether the assignment is correct. If it continues
correctly, it will be reinitialized incorrectly.
In the 15th experiment, a wooden board was suddenly
placed 5 cm in front of the car when the car was running
normally, and the car collided with the board and stopped.
The car has an ultrasonic obstacle avoidance module in
front of the design, but according to the principle of ultra-
sonic obstacle avoidance, we know that the ultrasonic obs-
tacle avoidance is limited by a certain distance. If the
distance is too short, the time difference between the
sounds and the rounds is small, and it is almost impossible
to distinguish, and the obstacle avoidance effect cannot be
achieved. In the future research, multi-sensor data fusion
technology can be fully utilized, and the ultrasonic sensor
and the infrared sensor are used to cooperate with each
other to complement each other to achieve better obstacle
avoidance effects. We tested 10 groups of obstacle avoid-
ance obstacles, 7 of which were successful in obstacle
avoidance and 2 were not shown in the table. The obstacle
is the tall plank we placed above the path. The car did not
successfully evade the obstacle, but collided with the
board because the obstacle was too high and the ultra-
sonic sensor did not detect the presence of the obstacle.
Under normal conditions, when the car drives to the
end of the path, there is no longer a sign of the path, the
car will stop, but in the experiment, the car stopped
when it was in the middle of the path. After analysis, we
found that the groups of experiments that had problems
occurred under the strong sunlight of direct traffic on
the road. Analysis of strong light illumination from
image processing may result in blurred images, so no
path is detected, resulting in operational parking. In
addition, there are two sets of data because in the ex-
periment we deliberately covered the path with dark
coverings. When the distance covered by the path is
short, the car can still judge the continuity of the path to
continue. When the width of the covering exceeds 10
cm, the trolley makes a judgment path terminal, and
thus, a parking operation is made.
In the car experiment, if there is a bad road condi-
tion, the road is not flat, and there are gullies, etc., it
will also affect the navigation accuracy. Therefore, in
the future research, the car body can be improved
from the mechanical structure, so that it can with-
stand the general condition. The reason is that the
road conditions are relatively stable in the actual
workshop application, and the bad road conditions ac-
count for a small number of sections. It is not the case
that the outdoor work situation is particularly strong
against the road conditions.
The working power supply is very important for the
normal operation of the car, which is the necessary en-
ergy. If there is no power supply, no matter how good
the controller is, it will not work. Although it is easy to
operate by means of a rechargeable battery, it requires
manpower to replace the battery or charge when the
power is exhausted. Therefore, future research can make
efforts in the automatic charging method to make the
automatic transport vehicle more intelligent.
Through the above analysis, it is known that the path
region is divided in the image as a target region for sub-
sequent processing to reduce interference caused by
other objects in the background. Then, the translation
vector is detected by the fast gray projection algorithm,
and the video image is corrected by motion compensa-
tion to achieve the image stabilization purpose.
Table 1 Partial experimental data
Test group Speed Distance Off-center distance Initialization
1 4000 15 m + 3 cm √
2 4000 15 m + 3 cm √
3 4000 15 m + 2 cm √
4 4000 15 m − 2 cm √
5 4000 15 m − 3 cm √
6 5000 15 m + 20 cm √
7 5000 15 m − 3 cm √
8 5000 15 m − 3 cm √
9 5000 15 m + 3 cm √
10 5000 15 m − 3 cm √
11 6000 15 m − 1 cm √
12 6000 15 m + 2 cm √
13 6000 15 m − 3 cm √
14 6000 15 m + 1 cm √
15 6000 15 m Stopped due to collision √
16 7000 15 m − 3 cm √
17 7000 15 m + 3 cm √
18 7000 15 m − 2 cm √
19 7000 15 m + 2 cm √
20 7000 15 m − 3 cm √
21 8000 15 m − 18 cm √
22 8000 15 m − 2 cm √
23 8000 15 m − 3 cm √
24 8000 15 m + 1 cm √
25 8000 15 m + 3 cm √
Jiang et al. EURASIP Journal on Image and Video Processing (2018) 2018:141 Page 7 of 8

8. 5 Conclusion
This study analyzes the path of port logistics transport
vehicles and studies several key technologies of the new
visual navigation automatic transport vehicles. Combin-
ing the actual needs of current port logistics and the
visual navigation in this paper, this study adopts mon-
ocular vision for logistics vehicle guidance, compara-
tively studies the mathematical models established by
past scholars, and establishes a simplified mathematical
model of automatic transport vehicles. In the fourth
chapter, in the research of the extraction of path infor-
mation, an algorithm for extracting the position of the
centerline from a path with a certain width is proposed
innovatively, and good results have been obtained
through experiments. In addition, this study established
the controller model of the automatic transport vehicle
and built the model under Simulink in Matlab to simu-
late the automatic transport vehicle to track the circu-
lar trajectory to run, analyze the error, and verify the
effectiveness of the method. Finally, we carried out the
actual performance test on an automatic transport ve-
hicle of the project team and tested the performance of
speed, precision, and motor running respectively. The
test results show the feasibility of the method.
Acknowledgements
The authors thank the editor and anonymous reviewers for their helpful
comments and valuable suggestions.
Funding
Research for this paper was supported by the Fundamental Research Funds
for the Central Universities (No.2019B12614) and the National Natural
Science Foundation of China (NO.41401120).
Availability of data and materials
Please contact author for data requests.
Authors’ contributions
All authors take part in the discussion of the work described in this paper. All
authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Received: 14 October 2018 Accepted: 27 November 2018
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