論文 - 大槻　知明

Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

Hu Y., Guan X., Hu B., Liu Y., Chen H., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 3 ） 2231 - 2244 2023年02月

　概要を見る

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

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Machine Learning in 6G Wireless Communications

Ohtsuki T.

IEICE Transactions on Communications （IEICE Transactions on Communications）  E106B （ 2 ） 75 - 83 2023年02月

ISSN  09168516

　概要を見る

Mobile communication systems are not only the core of the Information and Communication Technology (ICT) infrastructure but also that of our social infrastructure. The 5th generation mobile communication system (5G) has already started and is in use. 5G is expected for various use cases in industry and society. Thus, many companies and research institutes are now trying to improve the performance of 5G, that is, 5G Enhancement and the next generation of mobile communication systems (Beyond 5G (6G)). 6G is expected to meet various highly demanding requirements even compared with 5G, such as extremely high data rate, extremely large coverage, extremely low latency, extremely low energy, extremely high reliability, extreme massive connectivity, and so on. Artificial intelligence (AI) and machine learning (ML), AI/ML, will have more important roles than ever in 6G wireless communications with the above extreme high requirements for a diversity of applications, including new combinations of the requirements for new use cases. We can say that AI/ML will be essential for 6G wireless communications. This paper introduces some ML techniques and applications in 6G wireless communications, mainly focusing on the physical layer.

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Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

Yang S., Bouazizi M., Ohtsuki T., Shibata Y., Takabatake W., Hoshino K., Nagate A.

Future Internet （Future Internet）  15 （ 1 ）  2023年01月

　概要を見る

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep (Formula presented.) Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

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Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Bioengineering （Bioengineering）  10 （ 1 ）  2023年01月

　概要を見る

Objective: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. Main results: The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.

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Heart action monitoring from pulse signals using a growing hybrid polynomial network

Wang L., Zhao C., Mathiopoulos P.T., Ohtsuki T.

Engineering Applications of Artificial Intelligence （Engineering Applications of Artificial Intelligence）  117 2023年01月

ISSN  09521976

　概要を見る

Electrocardiogram (ECG) is a common used noninvasive test to quickly detect the heart problem with high precision. However, devices used to collect continuous ECG waveforms under free-living conditions have several operational difficulties. As an alternative, photoplethysmography (PPG) signals can be conveniently collected by pulse sensors, which can be mounted onto wearable devices. In this paper, we study the relation between ECG waveforms and PPG signals by proposing a novel growing hybrid polynomial network (GHPN)). Based on the projections between adjacent layers, the network is designed to approximate the target signals with the recorded inputs and the multi-scale dynamics of the input series are explored gradually through the growth of layers. Two public datasets are employed to evaluate the performance of the proposed approach on the accuracy of waveform reconstruction and heart rate (HR) detection with the widely used metrics. Compared with the reference ECG waveforms, the normalized mean square error (NMSE) of the proposed approach is 0.248 and 0.216 for PPG-Dalia and CapnoBase datasets, which is smaller than the comparison approaches. The average absolute value (AAE) between the detected HR and reference HR is 0.93 and 1.05 for PPG-Dalia and CapnoBase datasets, which exhibit higher HR detection accuracy. Experimental results obtained from benchmark datasets clearly show that the proposed method can achieve higher similarity on the reconstructed morphology with higher HR detection accuracy. Moreover, the proposed approach make it possible to employ PPG sensors for long-term monitoring of the heart actions with higher precision.

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Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval

Nakatani S., Yamamoto K., Ohtsuki T.

Bioengineering （Bioengineering）  10 （ 1 ）  2023年01月

　概要を見る

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection.

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A Reinforcement Learning-Based Fire Warning and Suppression System Using Unmanned Aerial Vehicles

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Transactions on Instrumentation and Measurement （IEEE Transactions on Instrumentation and Measurement）  72 2023年

ISSN  00189456

　概要を見る

This article discusses the use of unmanned aerial vehicles (UAVs) to detect, warn, and suppress forest fires. The proposed system consists of: 1) an energy harvesting-assisted surveillance UAV (SUAV) that detects and reports fires and 2) multiple ground-based firefighting UAVs (FUAVs) that travel to fire spots to suppress the fire. First, the SUAV carries out the forest fire detection using the onboard color and infrared cameras. The SUAV then uses fire alarm signals to warn/inform the ground-based FUAVs to begin the firefighting mission. The goal is to inform as many FUAVs as possible as the required task (extinguishing the fire) can be completed more effectively and faster with more FUAVs. To do this, the SUAV with onboard processing capabilities makes use of the advantages of reinforcement learning. In particular, we propose a double Q-learning-based trajectory design problem that enables the energy-constrained SUAV to find the optimal sequence of FUAVs to visit (optimal flying trajectory) in order to maximize the number of informed FUAVs while considering the limited execution time, as the crucial issue is to control fire from the beginning to prevent uncontrolled spread of fire.

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SAR Image Change Detection in Spatial-Frequency Domain Based on Attention Mechanism and Gated Linear Unit

Zhao C., Ma L., Wang L., Ohtsuki T., Mathiopoulos P.T., Wang Y.

IEEE Geoscience and Remote Sensing Letters （IEEE Geoscience and Remote Sensing Letters）  20 2023年

ISSN  1545598X

　概要を見る

Change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, first clustering is used as preclassification to obtain pseudo-labels and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. To improve the sensitivity of the algorithm to changed details and enhance the antinoise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multidomain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multiregion features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLUs) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).

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Multi-Person Position Estimation Based on Correlation between Received Signals Using MIMO FMCW Radar

Endo K., Ishikawa T., Yamamoto K., Ohtsuki T.

IEEE Access （IEEE Access）  11   2610 - 2620 2023年

　概要を見る

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-Angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person's position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-Angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

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Max-Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

Sun C., Xiong X., Zhai Z., Ni W., Ohtsuki T., Wang X.

IEEE Transactions on Wireless Communications （IEEE Transactions on Wireless Communications）  2023年

ISSN  15361276

　概要を見る

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.

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Investigation of Data Leakage in Deep Learning-based Blood Pressure Estimation using Photoplethysmogram/ Electrocardiogram

Yoshizawa R., Yamamoto K., Ohtsuki T.

IEEE Sensors Journal （IEEE Sensors Journal）  2023年

ISSN  1530437X

　概要を見る

Cuff-less blood pressure (BP) estimation methods using a deep learning model from photoplethysmogram (PPG) or electrocardiogram (ECG) have been actively studied in recent years. However, we found that most previous studies incur data leakage, where segments or records measured from the same subject appear in both the training and test datasets. Furthermore, many previous studies are suspected to have misinterpreted a record in the public dataset used for their evaluations as a subject. To investigate data leakage in BP estimation methods, this paper first organizes previous studies in terms of data leakage. We then quantitatively evaluate the effect of data leakage caused by the segment-level and the record-level train-test split using the public dataset, Cuff-Less Blood Pressure Estimation Data Set. Our experimental results showed that the segment-level split and record-level split erroneously improved the estimation accuracy of mean blood pressure from the (quasi-)subject-level split by the Pearson’s correlation coefficient of 0.56 and 0.40 when using PPG, and 0.82 and 0.69 when using ECG, respectively. These results confirmed that the train-test split used in many previous studies, including the one that described its evaluation as causing no data leakage, causes a high level of data leakage, and that a record in the Cuff-Less Blood Pressure Estimation Data Set, often misinterpreted as a subject in previous studies, is not a subject and that a high level of data leakage occurs when the record is considered as a subject.

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Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  2023年

　概要を見る

This paper discusses a cellular network assisted by an energy-and spectral-efficient unmanned aerial vehicle (UAV), in which the UAV is deployed to serve mobile users in the cellular network and enable mobile data offloading from a ground base station (GBS) by taking a circular flight route. We explore a visible light communication (VLC)-enabled UAV, in which a light-emitting diode (LED) is mounted on a rotary-wing UAV to offer communications to the users. Our aim is to simultaneously optimize both energy efficiency (EE) and spectral efficiency (SE) of the VLC-enabled UAV by jointly optimizing the common throughput of all users as well as the UAV’s trajectory and flying speed. We employ a unified metric, called resource efficiency (RE), and explore the RE optimization to obtain an adaptive EE-SE tradeoff. The problem posed is seen in a complex and non-convex shape, making it hard to solve. Motivated by the enormous achievement of deep reinforcement learning (DRL) in solving complex control problems, we propose a DRL-based approach to handle this non-convex and complicated optimization. The findings of the simulation reveal that the developed framework achieves a substantial performance in terms of the solution convergence as well as the promising quality of the solutions.

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Evaluation of Source Data Selection for DTL Based CSI Feedback Method in FDD Massive MIMO Systems

Inoue M., Ohtsuki T., Yamamoto K., Gui G.

Proceedings - IEEE Consumer Communications and Networking Conference, CCNC （Proceedings - IEEE Consumer Communications and Networking Conference, CCNC）  2023-January   182 - 187 2023年

ISSN  23319860

　概要を見る

In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) feedback method based on deep transfer learning (DTL) has been proposed to obtain the downlink CSI at the Base Station (BS). In the CSI feedback method based on DTL, a target model for one channel environment is obtained by fine-tuning the parameters of a source model trained on a large number of the CSI dataset (source data) of another channel environment. The fine-tuning is done with a small number of the CSI dataset (target data) of the target channel environment. Thus, a target model can be obtained at a low learning cost. However, the performance of the target model could highly depend on the source data. In this paper, we investigate two metrics as criteria for selecting source data to obtain a target model with a high CSI reconstruction performance: (i) Jensen-Shannon Divergence (JSD), which represents the similarity between target and source data, and (ii) entropy, which represents the diversity of source data. The simulation results showed when the target channel model is non line-of-sight (NLOS), the source data with high entropy and low JSD tend to provide higher CSI reconstruction performance of the target model. These results indicate that the JSD and the entropy could be a source data selection metric.

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Communication Efficient Heterogeneous Federated Learning based on Model Similarity

Li Z., Ohtsuki T., Gui G.

IEEE Wireless Communications and Networking Conference, WCNC （IEEE Wireless Communications and Networking Conference, WCNC）  2023-March 2023年

ISSN  15253511

　概要を見る

Federated Learning is now widely used to train neural networks under distributed datasets. One of the main challenges in Federated Learning is to address network training under local data heterogeneity. Existing work proposes that taking similarity into account as an influence factor in federated learning can improve the speed of model aggregation. We propose a novel approach that introduces Centered Kernel Alignment (CKA) into loss function to compute the similarity of feature maps in the output layer. Compared to existing methods, our method enables fast model aggregation and improves global model accuracy in non-IID scenario by using Resnet50.

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Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

F. H. Panahi, F. H. Panahi, and T. Ohtsuki

IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 最終著者

Detecting Dementia from Face-Related Features with Automated Computational Methods," Bioengineering

C. Zheng, M. Bouazizi,T. Ohtsuki, M. Kitazawa, T. Horigome, and T. Kishimoto

Bioengineering 2023年

研究論文（学術雑誌）, 責任著者

Automatic Modulation Classification Based on Decentralized Learning and Model Averaging

Xu Y., Ma M., Fu X., Wang Y., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in non-cooperative communication scenarios. Recently, benefitting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on local framework (LocalAMC) where there is only one device, or centralized framework (CentAMC) where multiple local devices (LDs) upload their data to the only one central server (CS). LocalAMC may not get ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS is quite massive. In this paper, we propose an AMC method based on decentralized learning with residual network (ResNet). Simulation results show that ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.

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Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training

Yoshizawa R., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications 2023-May   3401 - 3406 2023年

ISSN  15503607

　概要を見る

Blood pressure (BP) estimation methods using photoplethysmogram (PPG) based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, most previous studies are under data leakage, where subjects are not separated between training and test data. In this paper, we propose a method for BP estimation from PPG under the condition that subjects are separated between training and test data (inter-subject paradigm) using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features to improve the BP estimation accuracy under the inter-subject paradigm. Moreover, we use a considerably larger amount of training data than the previous studies and evaluate our model with smaller model sizes for better generalizability. Our experimental results showed that our method can estimate BP with moderate accuracy under the inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson's correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.

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An LSTM-Based Approach for Fall Detection Using Accelerometer-Collected Data

Uotani Y., Yamamoto K., Ye C., Bouazizi M., Ohtsuki T.

Proceedings 2023 28th Asia Pacific Conference on Communications Apcc 2023    250 - 255 2023年

　概要を見る

Over the past few years, there has been a significant rise in the number of fall accidents occurring among elderly individuals, a problem that has been accentuated with to the aging population. Researchers and developers have focused their efforts on investigating and creating various fall detection methods that utilize an accelerometer. However, conventional fall detection methods typically target specific positions where accelerometers are placed. In addition, they suffer from low accuracy which can be attributed to the fact that the classification algorithms commonly employed, such as the support vector machine (SVM) and the random forest (RF), are not specialized in making predictions based on time series data. In this paper, we propose the fall detection method based on a long short-term memory (LSTM) neural network, using an accelerometer. In the proposed method, four kinds of possession positions are set: (i) in hand, (ii) inside a chest pocket, (iii) inside a waist pocket, and (iv) in a bag. The acceleration data collected are classified using the LSTM classifies into one of four classes: (i) standing, (ii) walking, (iii) falling, and (iv) lying down. The results of the multi-class classification are further reclassified into two classes, i.e., fall and non-fall. The experimental results demonstrate that our approach outperforms the conventional methods in terms of fall detection accuracy.

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Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Zhang L., Wang Y., Ohtsuki T., Adebisi B., Wu H.C., Gui G.

2023 IEEE Cic International Conference on Communications in China Iccc 2023 2023年

　概要を見る

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%.

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