論文 - 大槻　知明

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

Pan G., Guan X., Jiang H., Liu Y., Wu H., Chen H., Ohtsuki T., Han Z.

Computer Networks 255 2024年10月

研究論文（学術雑誌）, 責任著者,  ISSN  13891286

　概要を見る

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user's charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G's high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user's range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

• Access to Document (DOI)

Time-Efficient Neural-Network-Based Dynamic Area Optimization Algorithm for High-Altitude Platform Station Mobile Communications †

Takabatake W., Shibata Y., Hoshino K., Ohtsuki T.

Future Internet 16 （ 9 ）  2024年09月

　概要を見る

There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution.

• Access to Document (DOI)

Deep Reinforcement Learning Based Data-Driven Mapping Mechanism of Digital Twin for Internet of Energy

Xu S., Guan X., Peng Y., Liu Y., Cui C., Chen H., Ohtsuki T., Han Z.

IEEE Transactions on Network Science and Engineering 11 （ 4 ） 3876 - 3890 2024年07月

研究論文（学術雑誌）, 責任著者

　概要を見る

Digital twin technology can be used in the internet of energy (IoE) systems to perform physical objects optimization and decision-making by updating relevant models in real-time. Model update is performed using real-time data generated by multiple devices contained in an IoE system. Since volume of the real-time data generated by IoE system is big, it is difficult for digital twin models to map the real-time data into virtual space. The difficulty of data mapping is because of high data volume and high resource consumption. In this paper, an adaptive data mapping mechanism using deep reinforcement learning (DRL) technique is devised. The proposed mechanism can dynamically adjust mapping data selection according to the environmental state and user requirements. By dynamically adjusting mapping data selection, the data and resource consumption used for mapping can be effectively reduced. The experimental results validate that the proposed mechanism can perform dynamic mapping data selection with reduced mapping data and low resource consumption.

• Access to Document (DOI)

Online Trajectory Optimization for Energy-Efficient Cellular-Connected UAVs With Map Reconstruction

Zhao H., Hao Q., Huang H., Gui G., Ohtsuki T., Sari H., Adachi F.

IEEE Transactions on Vehicular Technology （73, no. 3, pp. 3445-3456）  3 （ 73 ） 3445 - 3456 2024年03月

研究論文（学術雑誌）, 責任著者,  ISSN  00189545

　概要を見る

In this article, we leverage the outage probability knowledge map to characterize the connection between unmanned aerial vehicles (UAVs) and cellular networks. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers, which helps to enhance the UAV’s awareness of the environment and reduce the connection interruption under complex real-time channel state information. We assume that the UAV roughly samples from the actual radio environment of the airspace in advance, and calculates the outage probability of the sampled points. After that, the UAV reconstructs the actual outage knowledge map, and flies in the airspace to learn the optimal UAV trajectory planning policy based on the reconstructed map. The optimization objective is to minimize the flight energy cost of the UAV performing tasks. In this article, we propose a deep image prior based radio map reconstruction (DIPRMR) method to reconstruct the map, and then propose a deep reinforcement learning based trajectory optimization algorithm. The UAV that performs the task adjusts the flight trajectory based on the outage probability knowledge obtained from the reconstructed complete map. Simulation results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

• Access to Document (DOI)

SAR Image Wake Detection Based on Pseudo-Siamese Structure and Multidomain Feature Fusion

Zhao C., Liu H., Wang L., Ohtsuki T., Adachi F.

IEEE Geoscience and Remote Sensing Letters 21 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  1545598X

　概要を見る

The wake target has garnered increasing attention due to its length, which can be up to ten times that of the ship, and its inclusion of critical navigation information such as heading and speed. However, deep learning methods used in synthetic aperture radar (SAR) image wake detection tasks are limited to analyzing the features of the image itself, overlooking the characteristics of ship wakes in the frequency domain. This letter proposes a network called pseudo-siamese and multidomain feature fusion network (PSMDNet) that is composed of two parallel feature extraction branches. The feature extraction in the frequency domain uses the frequency channel attention network (FcaNet) as the backbone, incorporating an adjacent scale space attention module (ASSAM) to fuse high-level features into low-level features. The time domain uses the residual network (ResNet) as the backbone, incorporating a bidirectional feature channel module (BFCM) to enhance the representation of low-level spatial information. These two parallel branches extract the time- and frequency-domain features from the image to better capture the wake feature information. The proposed ASSAM module calculates weighted coding with context information, thereby selectively aggregating the unique linear spatial features of the wake into the low-level feature map. Verification experiments were conducted on the SAR-WAKE dataset, and the results demonstrate that the proposed method excels in detection accuracy compared with other algorithms, achieving excellent results of 92.71%. Particularly noteworthy is that the positioning and visualization of wake vertex and Kelvin arms are realized by the loss function designed for the wake.

• Access to Document (DOI)

Dementia Detection from Speech: What If Language Models Are Not the Answer?

Bouazizi M., Zheng C., Yang S., Ohtsuki T.

Sensors 15 （ 1 ）  2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Language Processing (NLP), these techniques have shown great potential in dementia detection. In this context, this paper proposes a method for dementia detection from the transcribed speech of subjects. Unlike conventional methods that rely on advanced language models to address the ability of the subject to make coherent and meaningful sentences, our approach relies on the center of focus of the subjects and how it changes over time as the subject describes the content of the cookie theft image, a commonly used image for evaluating one’s cognitive abilities. To do so, we divide the cookie theft image into regions of interest, and identify, in each sentence spoken by the subject, which regions are being talked about. We employed a Long Short-Term Memory (LSTM) neural network to learn different patterns of dementia subjects and control ones and used it to perform a 10-fold cross validation-based classification. Our experimental results on the Pitt corpus from the DementiaBank resulted in a 82.9% accuracy at the subject level and 81.0% at the sample level. By employing data-augmentation techniques, the accuracy at both levels was increased to 83.6% and 82.1%, respectively. The performance of our proposed method outperforms most of the conventional methods, which reach, at best, an accuracy equal to 81.5% at the subject level.

• Access to Document (DOI)

Towards Building Trust in Machine Learning Models: Quantifying the Explainability by SHAP and References to Human Strategy

Z. Li, M. Bouazizi, T. Ohtsuki, M. Ishii, and E. Nakaraha

Sensors 2024年

責任著者

Activity Detection in Indoor Environments Using Multiple 2D Lidars

Bouazizi M., Mora A.L., Feghoul K., Ohtsuki T.

Sensors 24 （ 2 ）  2024年

研究論文（学術雑誌）, 最終著者,  ISSN  14248220

　概要を見る

In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology.

• Access to Document (DOI)

Self-Supervised Learning Malware Traffic Classification Based on Masked Autoencoder

Xu K., Zhang X., Wang Y., Ohtsuki T., Adebisi B., Sari H., Gui G.

IEEE Internet of Things Journal 11 （ 10 ） 17330 - 17340 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

Malware traffic classification (MTC) is one of the important techniques to ensure the security of cyberspace, which aims to detect anomalies and classify different types of network traffic. Recently, MTC methods based on deep learning (DL) have shown their excellent performance. However, these DL-based methods rely on data sets with manually labeled samples for training, which are costly and hard to obtain. To address this problem, this article proposes a novel self-supervised MTC method based on the framework of masked auto-encoder (MAE). Specifically, MAE first constructs a reasonable unsupervised pretext task with a random masking strategy, which reduces the redundant information in samples and speeds up the pretraining process. The transformer-based backbone network then efficiently extracts features from the nonredundant traffic data efficiently. The proposed MTC-MAE method employs self-supervised learning on a large-scale unlabeled data set to acquire unbiased features, and fine-tunes on specific data sets to adapt to diverse traffic classification scenarios. Simulation experiments show that our proposed MTC-MAE method is able to learn universal features with high quality and has excellent classification performance on various downstream data sets. The data sets we used, code implementation, and pretrained models are available on GitHub. Code available at https://github.com/TsuiHark/Self-supervised_MTC.

• Access to Document (DOI)

Deep Learning Methods for Secure IoT SWIPT Networks

Ganapathy V., Ramachandran R., Ohtsuki T.

IEEE Internet of Things Journal 11 （ 11 ） 19657 - 19677 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

In this article, a deep-Q network (DQN)-based approach is proposed to improve the harvested energy and the secrecy capacity by optimizing the beamforming vectors in multiple-input-multiple-output (MIMO) networks with simultaneous wireless information and power transmission (SWIPT). Here, in the presence of multiantenna eavesdroppers, a multiantenna hybrid access point (HAP) serves energy harvesting Internet of Things (EH-IoT) nodes and information decoding (ID) receiver. The EH-IoT nodes and the ID receiver are separated from each other and experience different channels from the HAP. Two cases of signal models are studied to maximize the harvested energy. The first case is the consolidated signal case, where the HAP transmits an information-bearing signal to both the EH-IoT nodes and the ID receiver. The second case is the split signal case, where the HAP sends both information-carrying signal and energy signals to the EH-IoT nodes and the ID receiver. For both these cases, the optimization strategies for maximizing the harvested energy under Quality-of-Service (QoS) constraints are formulated with the secrecy capacity and the harvested energy requirements. The harvested energy maximization problems are complex to handle due to the presence of high-dimensional variables. An efficient, robust, and secure DQN-based algorithm is proposed, where Q-learning and deep neural network (DNN) methods are extended to solve these problems. Simulation results show that the proposed DQN approach can achieve a balance between harvested energy and security. By incorporating action and reward spaces that are adaptable to the formulated problem, significant performance gains are achieved.

• Access to Document (DOI)

Advancing Malware Detection in Network Traffic with Self-Paced Class Incremental Learning

Xu X., Zhang X., Zhang Q., Wang Y., Adebisi B., Ohtsuki T., Sari H., Gui G.

IEEE Internet of Things Journal 11 （ 12 ） 21816 - 21826 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

Ensuring network security, effective malware detection (MD) is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining memory for previously encountered malware categories as new ones emerge. To tackle these challenges, we propose a cutting-edge approach using self-paced class incremental learning (SPCIL). This method harnesses network traffic data for enhanced class incremental learning (CIL). A pivotal technique in deep learning, CIL facilitates the integration of new malware classes while preserving recognition of prior categories. The unique loss function in our SPCIL-driven MD combines sparse pairwise loss with sparse loss, striking an optimal balance between model simplicity and accuracy. Experimental results reveal that SPCIL proficiently identifies both existing and emerging malware classes, adeptly addressing catastrophic forgetting. In comparison to other incremental learning approaches, SPCIL stands out in performance and efficiency. It operates with a minimal model parameter count (8.35 million) and in increments of 2, 4, and 5, achieves impressive accuracy rates of 89.61%, 94.74%, and 97.21% respectively, underscoring its effectiveness and operational efficiency.

• Access to Document (DOI)

People Identification in Private Car Using 3D LiDAR with Generative Image Inpainting and YOLOv5

Shao W., Bouazizi M., Meng X., Ohtsuki T.

IEEE Access 12   38258 - 38274 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

People Identification is a critical aspect in developing modern vehicles, aimed at enhancing safety and comfort levels. Most traditional methods of people identification in vehicles use RGB images or videos. In this study, we introduce a novel methodology for identifying individuals in private car scenarios, utilizing 3D Light Detection and Ranging (LiDAR) technology, generative image inpainting based on Contextual Attention, and the YOLOv5 model. Initially, we gather data utilizing a 3D-LiDAR instrument and subsequently convert the acquired depth data into depth images. Following this, the depth images are annotated manually to indicate the positions and identifiers of various individuals occupying distinct seats. This annotated data serves as the training material for the YOLOv5 model, facilitating the recognition and categorization of subjects. However, given that individuals seated in the back often have parts of their bodies occluded by the front seats and the passengers in them, we employ generative image inpainting techniques to reveal the occluded portions. This step significantly enhances the precision in detecting and identifying individuals situated in the back seats. We implemented our strategy on a restricted group of four participants, conducting training and testing phases within identical environments. Prior to the inpainting process, the classification's F1 score stood at 66.5%. After inpainting, we observed a notable surge in the F1 score for the rear-seat passengers increased by 17.1%.

• Access to Document (DOI)

Toward Robust Open-Set Radiofrequency Signal Identification in Internet of Things Using Hypersphere Manifold Embedding

Fu X., Wang Y., Lin Y., Ohtsuki T., Gui G., Sari H.

IEEE Internet of Things Journal 11 （ 24 ） 41235 - 41247 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

Radiofrequency signal identification (RSI) provides a critical security solution for device authentication in the Internet of Things (IoT), characterized by extensive interconnections and interactions among numerous entities. By analyzing received radiofrequency signals, device-specific features are extracted at the receiver and used for identification. In a dynamic and ever-changing communication environment, where some devices not visible during the training process may appear during testing, a robust RSI method must not only identify devices encountered during training but also reject those that were not. In this article, we propose an open-set RSI method based on hypersphere manifold embedding. This approach leverages hypersphere projection for radiofrequency signal feature extraction on a hypersphere manifold, thereby avoiding the need to optimize intradevice variation in the radial direction. Additionally, we introduce an open-set identification approach based on generalized Pareto distribution, which does not rely on any radiofrequency signals from unknown devices. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art identification performance.

• Access to Document (DOI)

ime-Efficient Neural-Network-Based Dynamic Area Optimization Algorithm for High-Altitude Platform Station Mobile Communications

W. Takabatake, Y. Shibata, K. Hoshino, and T. Ohtsuki

Future Internet 2024 2024年

研究論文（学術雑誌）, 最終著者

Improved Deep Learning Methods for Beamforming in Secure SWIPT Networks

Ganapathy V., Ramachandran R., Ohtsuki T.

IEEE Transactions on Green Communications and Networking 9 （ 3 ） 962 - 976 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

Simultaneous Wireless Information and Power Transfer (SWIPT) is a unified approach to transfer power and information by exploiting the properties of radio signals. This paper considers a multi-antenna base station, multiple energy harvesting receivers (EHRs), and legitimate receivers in the presence of multiple multi-antenna eavesdroppers. A resource allocation problem of maximizing the harvested energy available at the EHR side in the presence of secrecy capacity and quality of service constraints is formulated for analysis. The transmit power, secrecy, and total transmit power requirements are used to formulate the constraints. We then propose neural network methods that use the channel state information data and constraint characteristics to obtain novel activation functions that can be used to predict beamforming vectors. Simulation results show that the proposed activation functions predict beamforming vectors to achieve better harvested energy and secrecy capacity.

• Access to Document (DOI)

Depth-video based Secondary Action Recognition in Vehicles via CNN and Bi-LSTM with Spatial Enhance Attention Mechanism

W. Shao, M. Bouazizi, and T. Ohtsuki

Sensors 2024 2024年

研究論文（学術雑誌）, 最終著者

Heartrate Estimation Considering Reconstructed Signal Features based on VMD Via MIMO FMCW Radar

S. Nakatani, M. Bouazizi, and T. Ohtsuki

Sensors 2024 2024年

研究論文（学術雑誌）, 最終著者

Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning

Ye C., Xu S., He Z., Yin Y., Ohtsuki T., Gui G.

Bioengineering 11 （ 11 ） 1124 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the deep learning technique, numerous Wi-Fi-based activity recognition methods can realize satisfied recognitions, however, these methods may fail to recognize the activities of an unknown person without the learning process. In this study, using channel state information (CSI) data, a novel cross-person activity recognition (CPAR) method is proposed by a deep learning approach with generalization capability. Combining one of the state-of-the-art deep neural networks (DNNs) used in activity recognition, i.e., attention-based bi-directional long short-term memory (ABLSTM), the snapshot ensemble is the first to be adopted to train several base-classifiers for enhancing the generalization and practicability of recognition. Second, to discriminate the extracted features, metric learning is further introduced by using the center loss, obtaining snapshot ensemble-used ABLSTM with center loss (SE-ABLSTM-C). In the experiments of CPAR, the proposed SE-ABLSTM-C method markedly improved the recognition accuracies to an application level, for seven categories of activities.

• Access to Document (DOI)

HAPS Cell Design Method for Coverage Extension Considering Coexistence on Terrestrial Mobile Networks

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Access 12   55506 - 55520 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

High-altitude platform stations (HAPSs) that provide direct communication services to smartphones on the ground are garnering significant attention as innovative mobile communication platforms suitable for ultrawide coverage areas. The deployment of a multi-cell configuration is essential for enhancing communication capacity over large geographical regions. However, designing and optimizing multi-cell configurations proves challenging due to the numerous antenna parameters involved, such as beamwidth and beam direction. HAPSs also hold promise in serving as reliable networks during disasters, as they can quickly restore coverage when terrestrial base stations (BSs) are in failure, allowing users to maintain connectivity via HAPSs using the same frequency band as terrestrial networks. Despite this advantage, HAPSs can potentially cause interference in terrestrial networks during such scenarios, necessitating careful considerations to avoid disrupting existing terrestrial networks. However, the optimization of cell configurations in the coexistence of HAPSs and terrestrial networks remains largely unexplored. To address this gap, this paper proposes a design scheme that leverages a genetic algorithm (GA) to optimize antenna parameters for each cell, thereby maximizing both user coverage and area coverage while ensuring coexistence with terrestrial networks. We optimize antenna parameters to increase user coverage and area coverage as a multi-objective optimization problem, resulting in a Pareto front that illustrates the trade-off choices. Additionally, the proposed method protects terrestrial networks by setting the initial beam direction of each cell to avoid interference with terrestrial BSs. Consequently, the GA can initiate optimization with candidate combinations that adhere to the interference constraints of terrestrial networks, thus expediting the optimization process and yielding improved coverage. Simulation results demonstrate that the proposed scheme outperforms conventional schemes which solely control beam direction, leading to superior coverage. While the results show trade-off between user coverage and area coverage, it is also clarified that considering both types of coverage show the potential to achieve significant improvements in both aspects simultaneously. Moreover, the results highlight the efficacy of the initial beam direction setting in enhancing coverage rates and significantly reducing the number of combinations required for the GA to converge.

• Access to Document (DOI)

Regularized Multi-Label Learning Empowered Joint Activity Recognition and Indoor Localization With CSI Fingerprints

Wang Y., Zhao H., Ohtsuki T., Sari H., Gui G.

IEEE Transactions on Wireless Communications 23 （ 11 ） 16865 - 16874 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  15361276

　概要を見る

Contactless Wi-Fi sensing, using channel state information (CSI) fingerprints, plays a pivotal role in communication, smart healthcare, and industrial automation. Deep learning has revolutionized the efficiency of non-contact sensing technology. Owing to its robust feature extraction capabilities and the interconnectedness of diverse sensing tasks, methods that address multiple tasks at once, like joint activity recognition and indoor localization (JARIL), have gained prominence. The primary goal of JARIL is to improve performance while reducing computational demands. Nevertheless, there remains substantial potential for enhancing its effectiveness through additional refinement and optimization measures. To address this, we introduce a regularized multi-label learning (RML) framework specifically designed for JARIL. This framework combines a parameter-efficient backbone network based on multi-scale separable convolution with residual connections, and a regularization training strategy. The latter strategy boosts performance by linearly combining two distinct CSI samples with their labels, creating new training instances in the training process. Simulation results show that the proposed method boasts a recognition accuracy of 91.73% and a localization precision of 99.64%. This marks an improvement of 4.32% and 3.60% respectively, in comparison to the prior ResNet1D+-based JARIL method. The codes can be downloaded from https://github.com/BeechburgPieStar/JARIL.

• Access to Document (DOI)