IEEE Systems, Man and Cybernetics Magazine - April 2021 - 30

proposed to overcome the shortcomings of traditional RNN.
LSTM-RNN models long-term dependencies, and each hidden layer is introduced with the concept of storage units.
The key to LSTM is the cell state, which runs directly on the
entire chain, with a few linear interactions only. It is easy for
information to circulate and remain unchanged. LSTM has
three gates to protect and control the cell state, including
the input, output, and forget gates. The forget gate determines what information is discarded from the cell state; the
input gate determines what new information is stored in the
cell state; and the output gate determines what value is output. This output is based on the cell state.
Let x denote the input historical flow sequence. H
denotes weight matrices. Let ig , fg , og , and Cs denote
the input gate, forget gate, output gate, and cell activation vectors, respectively, all of which are the same size
as the hidden vector h. v (x) is a standard logistic sigmoid function.
	
	
	
	

1
v ^ x h = 1 + e -x , (24)

fg t = v ^ H t # h t - 1 + H t # x t + b t h, (25)

ig t = v ^ H ig # h t - 1 + H ig # x t + b ig h, (26)

Cs 't = tan h ^ H Cs # h t - 1 + H Cs # x t + b Cs h, (27)

	

og t = v ^ H og # h t - 1 + H og # x t + b og h, (28)

	

h t = o t # tanh (Cs t), (29)

	

Cs t = ft # Cs t - 1 + i t Cslt . (30)

For the LSTM network model in this article, there is an
input, hidden, and output layer. The number of units in
each hidden layer is 20. The input incorporates the following features:
◆◆ holiday: 1, if it is a legal holiday; otherwise, 0
◆◆ weekend: 1, if the day is Friday to Sunday; otherwise, 0
◆◆ weather: 1, if the weather is good for the students to go
to the instructional buildings; otherwise, 0

6.33%

Lecture Halls

31.18%
62.49%

Large Classrooms
Small Classrooms

Figure 11. The distribution of occupants in various

classrooms.

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IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Apri l 2021

◆◆ time: ranges from 6:31 to 11:30 p.m.
◆◆ turnout: an empirical value that denotes the total num-

ber of students who come to the instructional buildings
from 6:31 to 11:30 p.m. to study.
The input data are normalized. The output is the count of
the net inflow of students. When the conditions of the scheduling algorithm to power on an additional classroom are
met, LSTM is adopted to predict the count of net inflow of
incoming students in the next 15 min. If the prediction number of students is fewer than the current number of remaining popular seats, one more classroom will not be powered
on, and the prediction will be made again after 5 min; until
the prediction number of seats is greater than or equal to the
number of popular seats, one additional classroom will be
powered on.
Experiments
Data Description
For LSTM, based on the result of the questionnaire surveys
of students' self-study in the evening and field surveys, the
simulation generates a data set for 90 days, including the students' arrival time, leaving time, date, weather, and seat
selection preferences. The data are sorted according to the
input format of LSTM. Then, 80% are used as the training set,
and 20% are used as the test set.
For scheduling, we developed a program to simulate the
occupant arrival and leaving of three groups, 1,500, 3,000,
and 5,000 people, respectively. The simulation runs on each
set of data 10 times independently. The number of students
on holidays and workdays during phases 1 and 2 are 1,500
and 3,000, respectively. The number of students during
phase 3 is 5,000.
Experiment Results and Analysis
The distribution of occupants in the three types of classrooms is depicted in Figure 11. The numbers of occupants in lecture halls, large classrooms, and small
classrooms account for approximately 62.49, 31.18, and
6.33%, respectively.
Two scheduling algorithms (PSO and ABC) are developed as benchmarks to compare with the aforementioned
ICS algorithm. The scheduling algorithms obtained i i and
H i with i ! {1, 2, 3} to specify the conditions for powering
on an additional classroom. Table 1 presents the data for the
simulations. In the simulations, it is supposed that for the
three scenarios of independent simulations 1) the total number of occupants in the lecture halls are 1,000, 2,000, and
3,000, respectively; 2) the total number of occupants in the
large classrooms are 500, 1,000, and 1,500, respectively; and
3) the total number of occupants in the small classrooms are
100, 200, and 300, respectively. The occupant flow is generated by simulations in terms of the arrival and leaving time distributions shown in Figures 1 and 2.
The main parameters of the other algorithms are set
as follows.



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