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Fidelity Assessment of Boeing 737-800 Simulator Via Manual Flying Touch and Go

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  • Malaysian Institute of Aviation Technology

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The acquirement of the Simulator Boeing 737-800 by Universiti Kuala Lumpur Malaysian Institute of Aviation Technology (UniKL MIAT) posed a technical challenge to the Assessment Team which was required to evaluate the fidelity of the simulator. This paper outlined the assessment of the fidelity of the simulator via a series of manual flying where numerous touch and go flights were actuated to examine the integrity of the simulator. Results indicated that several tinkering ought to be actuated to increase the fidelity of the simulator.
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International Journal of Scientific and Research Publications, Volume 12, Issue 3, March 2022 355
ISSN 2250-3153
This publication is licensed under Creative Commons Attribution CC BY.
http://dx.doi.org/10.29322/IJSRP.12.03.2022.p12349 www.ijsrp.org
Fidelity Assessment of Boeing 737-800 Simulator Via
Manual Flying Touch and Go
Mohd Harridon1,2,3, Mohd Khir Harun1, Mohd Ismawee Abdul Malik1, Mohamed Idrus Abd Moin1, Baha Rudin
Abdul Latif1, Muhamed Roihan Yusoff1, Azizihadi Yaakop1, Mohd Jalal Amran1
1Universiti Kuala Lumpur Malaysian Institute of Aviation Technology
2Malaysia Civil Defence Force
3Sky Orbital Centre for Human Advancement
DOI: 10.29322/IJSRP.12.03.2022.p12349
http://dx.doi.org/10.29322/IJSRP.12.03.2022.p12349
Paper Received Date: 7th March 2022
Paper Acceptance Date: 16th March 2022
Paper Publication Date: 20th March 2022
Abstract- The acquirement of the Simulator Boeing 737-800 by
Universiti Kuala Lumpur Malaysian Institute of Aviation
Technology (UniKL MIAT) posed a technical challenge to the
Assessment Team which was required to evaluate the fidelity of
the simulator. This paper outlined the assessment of the fidelity of
the simulator via a series of manual flying where numerous touch
and go flights were actuated to examine the integrity of the
simulator. Results indicated that several tinkering ought to be
actuated to increase the fidelity of the simulator.
Index Terms- Flight Test, Fidelity, Boeing 737-800, Flight Sorties
I. INTRODUCTION
oeing is a multinational company which had produced
various aircrafts of high stature. Many variants were
available, starting from 707 till 787 and continuously Boeing is
churning out several more variants. Boeing philosophy is to
transverse passengers from one destination to another in terms of
massive number of passengers. Hence, the introduction of
airplanes that could fit more than 100 passengers at one time. The
Jumbo is one good example of an airplane that could fit more than
400 passengers at one time.
According to Raheem and et al, for a passenger airplane that
transport high number of passengers, the lift coefficient of its
airfoil is high and this is in evidence as shown by the Boeing 747
[1]. Thus, before any flights are to be actuated, it’s imperative for
the crew be to accustomed to the handlings of the airplane since
each variant has different handling qualities. This is also to ensure
the crew are properly prepared to handle any dynamic anomalies
that exist during any physical maneuvers.
This is where the simulator comes in. In order to gain high
realism, the simulator has to be in pristine condition and be able to
simulate real life characteristics of real-life airplanes. The team
that accepts the simulator has to ensure the above conditions are
met. Harridon pointed out that there should be a defined structure
to test the simulator before it is officially accepted [2]. Harridon
went on further to state that the steps taken should be discreet and
in depth where each component of the simulator has to be retested
frequently in order to gauge its consistency in terms of
performance [2].
Our fidelity assessment of the Boeing 737-800 Simulator is
within the realm of Manual Flying Touch and Go where during the
initial flight test of the simulator we had encountered frequent
anomalies and system crashes whenever touch and go flights
(manual flights) were actuated. We thus proceeded to make a
structured account of these anomalies or system crashes which
were presented here in this paper. Goblet and et al had indicated
that the flight Touch and Go is a process which combined phases
of landing and takeoff where this is considered a hazardous event
that is of concern in terms of safety [3]. Thus, the actuation of
Touch and Go (Manual Flying) should be done by a pilot which is
skillful in maneuvering the aircraft. Our team consists of Harridon
which flew the aircraft at several different touchdown speeds
(within the Touch and Go Sorties) in order to gauge the resiliency
of the system of the simulator.
We were made aware by the manufacturer that frequent high
loads would create certain anomalies in the simulator. For
example, it was recommended (by the manufacturer) to reset the
simulator after 4 hours of continuous utilization of the simulator
in order to refresh the memory of the simulator and to prevent
existence of anomalies. The Touch and Go Sorties (Manual Flight)
that we actuated created high loads and anomalies began to
populate the simulator and we had recorded these anomalies in
structural form.
Our situation was not remote as other simulators of other
organizations faced similar predicaments. White and his team
reported that certain simulators were inaccurate in terms of their
dynamic maneuvers and do not accurately represent real flight
movements [4]. This itself is an anomaly and White and his team
further revealed that flights at low speed during complex
maneuvering would tend to introduce anomalies [4]. We
concurred with this assertion as certain flights of ours were at low
speed during touchdown and anomalies were apparent at these
junctures.
To solve the predicament of the simulator, it’s imperative to
pinpoint the problem and relate that with the uttermost parameter
that is relevant for flight operation. This is a well-defined approach
B
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and Harridon stated that an efficient framework is one that
addresses the difficulties and relatable to operations [5]. With this
approach, we had identified high loads as the predicament that
foster the anomalies. The Touch and Go Sorties were responsible
for these high loads and coincidently, as mentioned before, these
sorties are relevant to flight safety. Hence our fidelity assessment
through Touch and Go Sorties is validated.
II. LITERATURE REVIEW
Boeing is essentially the front runner in the aircraft industry
with huge number of aircraft sales. Most of the sales are within the
commercial sector which supplement the transportation needs of
the public. According to Irwin and Pavcnik, Boeing manufactured
narrow and wide bodies airplanes to cater for several destinations
which are short haul and long haul [6]. Irwin and Pavcnik also
stated that Boeing is not alone in this industry but has Airbus as its
primary rival [6]. This rivalry is healthy as both compete to
produce state of the art airplanes for consumers.
The Touch and Go Manual Flights that were actuated were
complex as they required high attention. According to Skybrary,
the Touch and Go maneuvers are demanding and challenging and
several aspects come into play such as the landing process and the
taking off process [7]. The combination of these and the short time
frame that was utilized for the processes had introduced high
amount of loads into the system of the simulator and thus we
highly suspected this to be the source of the anomalies.
The statement by Skybrary was concurred by Harridon where
Harridon stipulated that various flight incidents were due to
insufficient or inferior handling qualities of the aircraft [8].
Harridon further iterated that aircraft dynamics require the
individuals to understand and comprehend fully the mechanism of
flight of each flying vehicle [8].
Gizzi and et al mentioned that flight simulators have
ingrained anomalies and they had detected these anomalies using
Contextual Information [9]. Gizzi and et al even mentioned that
the most common anomalies in flight simulators are the internal
update counter of the simulator and fuel weight of the aircraft [9].
We were concerned with this as this correspond to our
predicaments where anomalies do exist at certain time frame and
scenarios of our simulator.
As stated earlier we utilized the approach of identifying the
problem in order to comprehend fully the characteristics of the
simulator. This is a norm as numerous researchers utilized this
approach. Chen and et al indicated that several methods exist to
identify problems and had mentioned root cause analysis and
event correlation techniques as methods to pin point predicaments
[10]. Chen and et al also stated that current techniques to identify
problems have drawbacks and these drawbacks would make the
identification inaccurate [10].
The identification of problems is useful as we could derive
appropriate solutions to increase the fidelity of the simulator as the
simulator is a vital tool to increase the proficiency of engineers
and pilots. According to Harridon there were various cases where
flight incidents or accidents occurred mainly because the pilots
were not proficient enough [11]. This is alarming and it is a safety
concern and hence it’s beneficial for pilots to go through training
using adequate and sufficient tools such as high-fidelity simulator.
In order to reap the full benefit of the simulator, it’s necessary
to retain and achieve a desired integrity of the simulator. Harridon
mentioned that carrying out the User Acceptance Test upon the
simulator would ensure the manufacturer gain feedbacks upon the
anomalies of the simulator and subsequently the manufacturer
would provide solutions to eradicate the anomalies [12]. This is
critical since there are ingrained anomalies upon any physical
products. Pavel and et al had studied tolerances in flight simulators
and they stated that anomalies do exist in simulators and there are
certain tolerances that could be accepted but with caveats [13].
Pavel and et al noted that certifications of simulators took into
account these tolerances [13]. Thus our fidelity assessment is
important in terms of structurally documenting the anomalies that
exist where future solutions could be developed based upon our
recorded documents.
III. METHODOLOGY
Our approach to gauge the fidelity of the Boeing 737-800
Simulator is shown in Figure 1. The approach takes into account
numerous anomalies where a structured reporting was actuated.
Figure 1. The Methodology for Fidelity Assessment of Boeing 737-800 Simulator
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We started off by identifying the predicament or the problem
of the simulator. Based upon the information from the
manufacturer and also based upon previous runs of the simulator,
high loads had been the prominent problem. We then proceeded to
identify the most vital aspect of flight operation. This is a no
brainer as safety is always the most important aspect in aviation.
With the two identified, the best or suitable activity to gauge the
fidelity of the simulator would be the Manual Flight Touch and
Go. We proceeded with the sorties and several parameters were
recorded. Those parameters were Date, Airport and Runway,
Touch and Go Attempt, Landing Speed, Description of
Anomalies, and other pertinent notes. The number of Touch and
Go actuated was based upon arbitrary decision and usually it was
ended when anomaly or anomalies were in existence. We then
discussed the results and made conclusions and we also offered
solutions to eradicate the recorded anomalies.
IV. RESULTS
The results of our fidelity assessment are shown in Table 1.
We included notes with regards to the switching OFF and ON of
the simulator. This is vital as the switching OFF of the simulator
had released high loads from the memory of the simulator and the
simulator would start “fresh”.
Table 1. Results of the Fidelity Assessment of Boeing 737-800 Simulator
Date
Airport / Runway
Touch and Go
Attempt
Landing Speed
Description of
Anomalies
26th January 2022
Kuala Lumpur
International Airport /
Runway 14R
5th Touch and Go
Attempt
130 KIAS
Master Caution
Annunciated just as the
airplane touched the
ground
27th January 2022
Kuala Lumpur
International Airport /
Runway 14R
1st Touch and Go
Attempt
140 KIAS
No Anomalies
2nd Touch and Go
Attempt
142 KIAS
No Anomalies
3rd Touch and Go
Attempt
139 KIAS
After Touch Down we
took off again and the
MCP was not
functioning as
described below :
- Could not set
Altitude
- Could not set
Heading
- Could not set
Speed
4th Touch and Go
Attempt
140 KIAS
No Additional
Anomalies
5th Touch and Go
Attempt
137 KIAS
No Additional
Anomalies
Notes :
The simulator was OFF and loaded back (Switch ON). Several failures existed after the simulator was fully loaded. Those failures
were : Engine Number 2 malfunctioned during starting of engine, Engine Number 1 malfunctioned during starting of engine
The simulator was OFF again and loaded back (Switch ON). Several failures existed after the simulator was fully loaded. Those
failures were : In the Dark and Cold Situation, all the lights in the overhead panel and other panels had lighted up
The simulator was OFF again and loaded back (Switch ON). Everything was back to normal with no anomalies.
28th January 2022
Langkawi International
Airport / Runway 03
1st Touch and Go
Attempt
139 KIAS
No Anomalies
2nd Touch and Go
Attempt
128 KIAS
No Anomalies
3rd Touch and Go
Attempt
130 KIAS
No Anomalies
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4th Touch and Go
Attempt
128 KIAS
No Anomalies
5th Touch and Go
Attempt
128 KIAS
After Touch and Go, a
system check was done
and it seems that the
glidescope indicator
(the magenta diamond)
was not present even
though the glidescope
had been captured by
the aircraft
Notes :
We left the simulator ON for more than 4.5 hours
28th January 2022
Hong Kong
International Airport /
Runway 07R
1st Touch and Go
Attempt
138 KIAS
- Le Flaps Ext
Sign
Annunciated
- The simulator
did not
recognize that
the Landing
Gear was
down
Notes :
The simulator was OFF and loaded back (Switch ON). There is one prominent failure which is the CDU where a huge X was
displayed on the CDU. The simulator was then OFF again and loaded back (Switch ON).
28th January 2022
Hong Kong
International Airport /
Runway 07R
1st Touch and Go
Attempt
140 KIAS
- Le Flaps Ext
Sign
Annunciated
- The simulator
did not
recognize that
the Landing
Gear was
down
Notes :
The simulator was OFF again. This time the Uninterruptible Power Supply (UPS) was OFF and then ON again. The simulator was
then loaded back (Switch ON).
28th January 2022
Hong Kong
International Airport /
Runway 07R
1st Touch and Go
Attempt
140 KIAS
No Anomalies
2nd Touch and Go
Attempt
130 KIAS
- Le Flaps Ext
Sign
Annunciated
- The simulator
did not
recognize that
the Landing
Gear was
down
Notes :
The simulator was OFF again. The Uninterruptible Power Supply (UPS) was also OFF again and then ON again. The simulator was
then loaded back (Switch ON).
28th January 2022
Changi International
Airport / Runway 02C
1st Touch and Go
Attempt
140 KIAS
- The simulator
stalled
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- The simulator
did not
recognize that
the Landing
Gear was
down
V. DISCUSSION
On 26th January 2022 several Touch and Go were actuated
and during the 5th Touch and Go, as the wheel touched the runway
during landing, an anomaly existed which was the annunciation of
the Master Caution. While on the 27th January 2022, during the 3rd
Touch and Go attempt, several anomalies existed after the airplane
had touched down and took off. We opinioned that high loads were
in existence after several Touch and Go were actuated and this had
led to the malfunctions.
It is interesting to note that after the simulator was OFF and
restarted, there existed several anomalies such as malfunctioned
of engines 2 and 1 during the starting phase of the engines. This
compounded us to OFF and ON the simulator again but the
overhead panel and other panels lighted up which is odd as the
aircraft was in a state of Dark and Cold. The simulator was
restarted again and all systems were back to normal.
On 28th January 2022 several Touch and Go were actuated at
Langkawi International Airport. There were no anomalies from 1st
till 4th Touch and Go. At the 5th Touch and Go, after taking off
after touch down, an anomaly was detected when a system check
was actuated. The glidescope indicator was not displayed even
though the aircraft had captured the glidescope. The simulator was
then left to be ON for more than 4.5 hours without any activities
(the airplane was stationary parked at the airport).
The Touch and Go Flights were continued at Hong Kong
International Airport Runway 07R. At the 1st Touch and Go,
anomalies existed when the airplane touched the ground during
landing. The Le Flaps Ext Sign had annunciated and also the
system did not recognize that the landing gear was down. We
proceeded to OFF the simulator and the simulator was restarted
again. At this juncture, the Control Display Unit (CDU) of the
airplane showed a big X which filled the screen of the CDU. The
simulator was OFF again and loaded again. The system was back
to normal and the Touch and Go Sorties were actuated. At the 1st
Touch and Go, similar predicaments occurred where the Le Flaps
Ext Sign had annunciated and the system did not recognize the
landing gear was down.
We proceeded to OFF the simulator but this time the
Uninterruptible Power Supply (UPS) was OFF. The simulator was
then restarted and the Touch and Go Sorties were actuated. No
anomalies were observed at the 1st Touch and Go but there were
numerous anomalies during the 2nd Touch and Go. The Le Flaps
Ext Sign had annunciated and the system did not recognize the
landing gear was down. The simulator and UPS were OFF again
and we proceeded to switch them ON. The Touch and Go Sorties
were then actuated again but this time at Changi International
Airport Runway 02C. At the 1st Touch and Go attempt, the
simulator stalled during landing and the simulator did not
recognize that the landing gear was down. We proceeded to OFF
the simulator and discontinued the Touch and Go Sorties.
It can be seen that in order for us to actuate the fidelity
assessment, we had done a mere categorization or classification as
shown in Table 1. Harridon indicated that classification is a good
approach to effectively identify entities of different realms [14].
Sun and Du concurred with this and they stated classification aids
in the extraction of relevant information [15].
VI. CONCLUSIONS
The Boeing 737-800 Simulator were tested to gauge its
fidelity. Several manual flights were actuated where Touch and
Go Sorties were flown manually and during those sorties
numerous anomalies existed. These anomalies were structurally
recorded and from there onwards solutions can be derived to
eradicate the anomalies. The best solution would be to enhance the
capacity of the simulator in order for it to handle heavy loads
during flight operations. Enhancement could be done by
increasing the memory of the system or by other means.
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AUTHORS
First Author Mohd Harridon, Universiti Kuala Lumpur
Malaysian Institute of Aviation Technology, Malaysia Civil
Defence Force, Sky Orbital Centre for Human Advancement,
mdharridon@unikl.edu.my
Second Author Mohd Khir Harun, Universiti Kuala Lumpur
Malaysian Institute of Aviation Technology
Third Author Mohd Ismawee Abdul Malik, Universiti Kuala
Lumpur Malaysian Institute of Aviation Technology
Fourth Author Mohamed Idrus Abd Moin, Universiti Kuala
Lumpur Malaysian Institute of Aviation Technology
Fifth Author Baha Rudin Abdul Latif, Universiti Kuala
Lumpur Malaysian Institute of Aviation Technology
Sixth Author Muhamed Roihan Yusoff, Universiti Kuala
Lumpur Malaysian Institute of Aviation Technology
Seventh Author Azizihadi Yaakop, Universiti Kuala Lumpur
Malaysian Institute of Aviation Technology
Eight Author Mohd Jalal Amran, Universiti Kuala Lumpur
Malaysian Institute of Aviation Technology
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Therefore, in order to meet the diverse and personalized needs of online news users in the context of the big data era, effective information organization and management of online news have become an urgent problem to be solved [7]. News text classification based on traditional machine learning mainly includes the following problems: on the one hand, the use of traditional news text representation methods to represent news texts will ignore the word order and semantic information contained in the news text; on the other hand, it will ignore the word order and semantic information contained in the news text. The process of feature extraction of news text requires manual participation, which is subjective and consumes more time and energy; at the same time, using vector space model to represent news text will cause high latitude and data sparsity problems, although the feature selection method can be used for dimensionality reduction, but this will further aggravate the problem of news text feature loss and make the entire news text classification process more complicated. These problems will directly affect the effect of news text classification. Therefore, it is necessary to seek more ingenious text feature representation and text feature extraction models. This paper mainly conducts research from two aspects: text feature representation and text feature selection in network news text classification. Traditional text classification methods have many difficult problems to solve. Aiming at the high-dimensional sparseness of traditional text representation methods in natural language, the embedding layer converts text data into low-dimensional dense vectors, avoiding the dimensional disaster caused by high-dimensional input. At the same time, by using word vectors, the impact of word segmentation errors on the accuracy of text classification tasks is avoided, and the performance of the classifier is improved. The process of feature extraction requires manual participation, which will affect the accuracy of the final extracted text features; using vector space model for text representation will ignore the word order and semantics in the text. Information affects the performance of text classification; in the face of high latitude and data sparsity problems, although feature selection methods can be used to reduce dimensionality, this will further aggravate the problem of text feature loss and make the entire text classification process more difficult. In order to solve the above problems, this article reexamines the traditional process of online news text classification based on the research of online news text classification and uses deep learning methods as the theoretical basis to reconstruct the process of online news text classification using deep learning related theories and models to achieve the purpose of solving the problems of traditional text classification and improving the effect of text classification. 2. Related Work Some related research on text classification can be traced back to the 20th century. Abdi et al. [8] proposed the concept of word frequency and applied the concept of word frequency statistics to text classification for the first time, laying a foundation for text classification and creating a precedent for text classification research. The method of text classification using keyword classification technology proposed by Wu et al. [9] was of milestone significance, which has greatly pioneered and promoted the research work of text classification. Subsequently, more and more researchers have carried out a series of studies and achieved many results, and text classification technology has received more research and applications. Many well-known intelligence scientists, such as Shon et al. [10], have carried out research in the field of classification and have achieved remarkable research results. At that time, text classification was mainly based on word matching, which determined the category of the document according to the frequency of common words in the text and category name. This classification method is simple and intuitive, the classification rules are very mechanical, and the classification effect is not good. In the future, text classification has gradually transitioned from the idea of word frequency statistics to a method based on knowledge engineering. This method needs to rely on experts to compile classification rules. Compared with the word matching method, it has a higher understanding of logical rules and the classification effect is also improved. Because documents may be different in length (that is, the number of words is different), in order to be able to feed to a fixed-dimensional neural network, we need to set a maximum number of words. For documents with the number of words less than this threshold, we need to use “unknown words” to go filling. For example, the word with index 0 in the vocabulary can be set as “unknown word,” and 0 is used to fill in the part less than the threshold. However, the formulation of such classification rules needs to rely on domain experts to manually compile for specific fields, which will consume a lot of time and energy, and cannot process text information with a large amount of data. And these rules are usually oriented to specific fields and between fields. The versatility is poor, and the range that can be covered is very limited. Therefore, methods based on knowledge engineering have not been widely used. With the rapid growth of text information resources on the Internet, text classification methods have received unprecedented attention. However, the text classification technology based on knowledge engineering has been completely unable to meet the demand, has been gradually replaced by the emerging statistics-based machine learning text classification method, and has quickly become the new mainstream method in the field of text classification, and it is still the focus of many scholars’ research. The text classification method based on statistical machine learning learns the sample data of known categories. Girgis et al. [11] use the learned category features to construct a classifier, then use the classifier to classify the text information to be classified, and finally obtain the text information. Compared with the method based on knowledge engineering, the construction of the classifier in this method does not require human involvement, which greatly reduces manpower and material resources. Pasupa and Ayutthaya [12] fused deep learning algorithms in different ways and compared it with several other fused combinations. In terms of text classification efficiency and accuracy, Buabin [13] proposes that they have a very significant improvement. It is precisely because of the more reliable theoretical basis and better classification results that the text classification method based on machine learning has received extensive attention from scholars [14], and it is still the focus and mainstream of researchers’ application and research, with a wide range of applications, such as text mining, pattern recognition, information retrieval, data mining, learning systems, and other fields [15–18]. Among the machine learning methods, the more commonly used are class center vector method, K-nearest neighbor method, and support vector machine method [19]. The research on text classification is relatively late. The earliest scholars summarized and introduced classification technology and research status, which drove scholars in the field of library and information research on Chinese text classification [20, 21]. As one of the core technologies of information resource organization and management, text classification has received great attention from many researchers [22–24]. Since then, researchers have combined the specific knowledge of Chinese texts and absorbed the results of English text classification. Reconstruction and optimization make it suitable for Chinese text classification. After continuous exploration and development by researchers, a Chinese text classification research system has been formed [25]. With the development of text classification technology, not only has it attracted the attention of many researchers, universities, research institutes, and enterprises at all levels in the country have also paid great attention to the research of text classification technology, not only related researchers and scholars who study text classification technology. With the strong support of many funds, there are more and more papers related to text classification, which promotes the rapid development of text classification research. The Chinese Academy of Sciences, Peking University, Harbin Institute of Technology, Tsinghua University, Shanghai Jiaotong University, and other universities and research institutes have conducted a lot of research in the field of text classification. After continuous exploration and research by researchers, my country has obtained fruitful research results in the field of Chinese text classification. Typical representative systems include the Zhiduoxing Chinese text classifier of the Institute of Computing Technology, the text classification of Fudan University, and the text classification of Peking University. Some of them have been successfully promoted and applied [26–28]. 3. Construction of the News Text Classification Model Based on Hybrid Deep Learning 3.1. News Text Feature Selection Feature selection is to select some of the most representative features of the text content from the original feature space for text classification without affecting the nature of the original feature space. The basic principle of feature selection is to rank the original text word sequence with the aid of the evaluation function. By selecting some relatively high score features as the final text feature, the dimensionality of the text feature space is reduced, thereby improving the Chinese language in the field of online news. Aiming at the problem of poor network news text classification caused by defects such as high vector latitude, sparse data, and lack of semantics in traditional machine learning news text classification methods, the word vector method is used to represent news text word sequences, which can be effective to solve these problems. In the optimization process, the introduction of momentum can speed up convergence and reduce meaningless oscillations. When the gradient points to the actual moving direction, the momentum term increases; when the gradient is opposite to the actual moving direction, it decreases. This ensures that the direction of optimization is always towards the minimum point, reduces unnecessary updates, and improves the optimization effect while reducing computational overhead. The main idea of the word vector is to map each word to the low-latitude space. In the new low-latitude feature space after the mapping, the positional relationship between the word vectors corresponding to different feature words represents the semantics between them. The association on the level can solve the problems of vector sparseness and semantic lack. Assuming that any piece of online news text in the experimental data set has k words, The convolutional neural network used in this paper draws on the model design of Kim’s paper, and the specific structure is shown in Figure 1. For each input element s, use s (x) to represent the value obtained after x is processed by the function. The leftmost input layer of the convolutional neural network structure is a k × n two-dimensional word vector matrix, where k represents the length of a news text composed of words k1, k2, k3,..., k−i, and n represents the length of each word vector dimension. The feature extraction part of the convolutional neural network structure mainly includes operations such as convolution and pooling. Finally, a corresponding model is constructed on the task of news text classification.
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