論文 - 大槻　知明

A 2D Lidar-equipped unmanned robot-based approach for indoor human activity detection

M. Bouazizi, A.-L. Mora, and T. Ohtsuki

Sensors 2023 2023年

研究論文（学術雑誌）, 共著, 最終著者

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

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

in IEEE Access 11   2610 - 2620 2023年

研究論文（学術雑誌）, 共著, 最終著者

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

K. Endo, K. Yamamoto, and T. Ohtsuki

Sensors 2022 2022年

研究論文（学術雑誌）, 共著, 最終著者

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

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

Future Internet 2023 2023年

研究論文（学術雑誌）, 共著

An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

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

in IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 共著, 最終著者

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

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

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

研究論文（学術雑誌）, 共著, 最終著者

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

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

IEEE Geoscience and Remote Sensing Letters 2023年

研究論文（学術雑誌）, 共著

Graph Learning Empowered Situation Awareness in Internet of Energy with Graph Digital Twin

L. Sui, X. Guan, C. Cui, H. Jiang, H. Pan and T. Ohtsuki

in IEEE Transactions on Industrial Informatics 2022年

研究論文（学術雑誌）, 共著, 最終著者

Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

D. Shi, L. Li, T. Ohtsuki, M. Pan, Z. Han and H. V. Poor

in IEEE Transactions on Mobile Computing 21 （ 12 ） 4426 - 4438 2022年12月

研究論文（学術雑誌）, 共著

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

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

Bioengineering 2023年

研究論文（学術雑誌）, 共著

Fetal Arrhythmia Detection based on Labeling Considering Heartbeat Interval

S. Nakatani, K. Yamamoto, and T. Ohtsuki

Bioengineering 2023年

研究論文（学術雑誌）, 共著, 最終著者

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

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

IEEE Transactions on Instrumentation and Measurement 2022年

研究論文（学術雑誌）, 共著, 最終著者

Heart Action Monitoring From Pulse Signals Using A Growing Hybrid Polynomial Network

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

Engineering Applications of Artificial Intelligence 117 2023年01月

研究論文（学術雑誌）, 共著, 最終著者

An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

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

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 11 ） 9646 - 9661 2023年06月

　概要を見る

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy Q-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional Q-learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

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Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things

Zhao R., Wang Y., Xue Z., Ohtsuki T., Adebisi B., Gui G.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 10 ） 8645 - 8657 2023年05月

　概要を見る

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: 1) model parameters transmitted in each round may be used to recover private data, which leads to security risks; 2) not independent and identically distributed (non-IID) private data seriously adversely affect the training of FL (especially distillation-based FL); and 3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this article develops an intrusion detection method based on a semisupervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a model based on convolutional neural networks (CNNs) for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, the discriminator is designed to improve the quality of each client's predicted labels, and to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of the hard-label strategy and voting mechanism further reduces communication overhead. The experiments on the real-world traffic data set with three non-IID scenarios show that our proposed method can achieve better detection performance as well as lower communication overhead than state-of-the-art methods.

• Access to Document (DOI)

Graph Learning Empowered Situation Awareness in Internet of Energy With Graph Digital Twin

Sui L., Guan X., Cui C., Jiang H., Pan H., Ohtsuki T.

IEEE Transactions on Industrial Informatics （IEEE Transactions on Industrial Informatics）  19 （ 5 ） 7268 - 7277 2023年05月

ISSN  15513203

　概要を見る

Internet of energy (IoE) is one of the most complex industrial systems, and its stable operation is very important. Situation awareness (SA) has been proposed to ensure the stable operation for IoE and making full use of the relationships between components has become the key point for designing an efficient SA model. In this article, graph digital twin (GDT) is proposed by combining digital twin technology with graph theory, to describe the logical relationships between physical entities more accurately in digital space, and then a novel SA model for IoE based on GDT is proposed. In order to make full use of the relationship between nodes, two classifiers based on graph convolution network are designed for fault location and stability prediction. The experimental results show that the proposed SA model can localize the multiple fault components with high accuracy, and can accurately predict the stability of the system.

• Access to Document (DOI)

A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection

Bouazizi M., Lorite Mora A., Ohtsuki T.

Sensors （Sensors）  23 （ 5 ）  2023年03月

ISSN  14248220

　概要を見る

Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively.

• Access to Document (DOI)

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.

• Access to Document (DOI)

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.

• Access to Document (DOI)

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.

• Access to Document (DOI)