論文

© 2019 IPSJ. Post-induction hypotension frequently occurred after anesthesia induction. Avoiding post-induction hypotension is important as it is associated with postoperative adverse outcomes. Related studies have shown that the dose of anesthetic induction drugs affects the post-induction hypotension. The purpose of this study is to propose an anesthetic dose that does not cause post-induction hypotension according to the patient's condition. A model for predicting the optimal dose of an anesthetic induction drug is constructed using a regression model which is one of machine learning methods by focusing on electronic anesthesia records. The prediction coefficient of determination 0.5008 was achieved by adjusting the explanatory variables and parameters and using ridge regression.

© 2019 IPSJ. Electronic anesthesia record data have been accumulated, and efforts to solve medical problems using data analysis methods and machine learning have been conducted. Post-induction hypotension frequently occurred after induction of anesthesia. Intraoperative hypotension is associated with various adverse events such as myocardial infarction and cerebral infarction. In a related study, eight machine learning methods were used to construct hypotension prediction models and evaluated by area under the curve (AUC), using data collected from an institution in the United States. Nevertheless, it was not focused on improving prediction power. This paper aims to predict post-induction hypotension with high prediction power using 1,626 electronic anesthesia record data. Our hypotension prediction model using a stacking method is introduced. F-measure 0.60 was achieved by using our method through the evaluation.

© 2019 IPSJ. Carrier sense multiple access/collision avoidance (CSMA/CA) in wireless local area network (WLAN) is considered as a complex protocol for modeling due to its autonomous function and influence of interference with medium shared by multiple nodes. IEEE 802.11ax, a sixth generation WLAN standard, enables various flows with different modulation coding scheme (MCS) and traffic load, transmission power (TxP), clear channel assessment threshold (CCAT) to coexist in a WLAN. This variation makes modeling of WLAN system more difficult. This paper proposes a throughput model for high efficiency WLAN analysis under any traffic load and MCS condition. This model factorizes throughput into five elements. This flexible model can be applied to any complex topology. In simulation evaluation, a correlation coefficient between the results of the proposed model and simulation is observed by at most 0.981. Furthermore, The results shows that the influence on the throughput due to the change in TxP is correctly modeled and minimum TxP for maximum throughput is revealed on a condition.

© 2019 IEEE. Mobile ad hoc networks (MANETs), where nodes can communicate without an infrastructure, require all nodes to be cooperative enough to transfer packets for other nodes. MANETs have been studied for decades, due to its characteristics which are infrastructureless and distributed. However, the existence of malicious nodes that drop packets for their resource protection is one of the important issues of the present research. Reputation systems using trust model have been proposed to detect malicious nodes in MANETs. Here, the trust value indicates the degree of reliance. One of the attacks in MANETs is On-Off attack on which attack nodes keep trust above a certain level by repeating forwarding and dropping packets. In this paper, we propose a trust model focusing on node usage against MANETs On-Off attack (TMUMO). In this study, we detect On-Off attack nodes by considering the characteristics of On-Off attack. Through simulation experiments, we improve detection rate of attack nodes and packet delivery rate in MANETs, and show feasibility of our trust model.

© 2019 IEEE. Wireless Local Area Networks (WLANs) have been deployed more densely according to high wireless demands. In a dense environment where a lot of stations (STAs) located closely, co-channel interference decreases system performance seriously. IEEE 802.11ax Task Group focuses on spatial reuse that utilizes finite frequency resources to address this problem. Although existing studies about spatial reuse have been discussed so far, interference from other basic service sets (BSSs) and cooperation with each access point (AP) have not been considered fully. To enhance communication fairness, we propose fair Dynamic Sensitivity and Transmission Power Control (fairDSTPC) that adjusts CCAT and transmission power based on AP cooperation. We improve communication environment of BSS which results in the worst throughput by exchanging information, such as throughput and number of sent data frames, between APs. Simulation results showed that our method achieved effectiveness compared with existing method and legacy system. In an open space scenario, our method reached approximately two times higher 5% ile downlink throughput than an existing method while maintaining 5% ile uplink throughput and aggregate throughput.

© 2018 IEEE. Collaborators are required to share a broad range of information in collaborative work. In physical spatial collaboration such as team dancing, theatrical performance, and team sport, one's behavior should be in accord with the partner's; the relative position in distance and in direction is critically important all the time even when moving. To realize remote physical spatial collaboration, achieving the shared recognition of positional relationship is one of the key issues. In this paper, we propose a setup between two remote sites where each collaborator immerses in the other remote site by a telepresence robot situated in the other site. The telepresence robot is a wheeled platform with a camera attached at man's eye height. It moves in response to the remote user's position and orientation and enables the user to get the first-person view video through a head-mounted display. By using two telepresence robots from both sites, both collaborators can recognize the partner being in front of him/her and can grasp the distance and the orientation of the partner in remote site. In the proposed setup, users can grasp the positional relationship as accurate and quick as the existing fixed views including the bird's-eye view.

© 2018 IPSJ. Dynamic Route Guidance (DRG) system, which determines the traveling routes of vehicles, alleviates traffic problems in urban areas. On the other hand, ad-hoc network composed of vehicles, called VANET attracts attention from many researchers and VANET-based DRG systems are proposed. However, the existing DRG systems do not consider that degradation of communication performance causes degradation of application performance. Packet loss of traffic information hinders determining proper travelling routes. In this paper, we propose VANET-based DRG system with dynamic communication path selection by considering vehicle density, called DRG-DCPS. Each vehicle acquires traffic information by a request/response packet. Relay vehicles select crowded road segments as packet paths to improve communication performance. As a result, since each vehicle receives traffic information more correctly and can select appropriate travelling routes. To confirm effectiveness of DRG-DCPS, we implemented on a simulation environment integrating traffic simulator and network simulator.

© 2017 IPSJ. This paper proposes a method to estimate the preference of customers based on store check-in histories. The proposed method can distinguish the preferences of customers who have no purchase histories. We adopt a machine learning algorithm for model acquisition. The estimation results can improve the efficiency of visitor promotion campaigns and advertising campaigns. An actual visitor promotion trial indicates the effectiveness of the proposed method.

© 2018 IEEE. The explosive usage of IEEE 802.11 Wireless Local Area Network (WLAN) has resulted in its dense deployments and excessive interference between Basic Service Sets (BSSs) in urban area such as an apartment building and an airport. Serious problems of hidden/exposed terminal in high-density condition negatively impact system throughput. To improve the system efficiency, IEEE 802.11ax TG has been assembled. TG aims at realizing High-Efficiency- WLAN (HEW) by utilizing special reuse technologies including Dynamic Sensitivity Control (DSC), Transmit Power Control (TPC), and BSS Color Filtering (BCF). In this paper, we propose a DSC based on two-hop farthest terminal for dense WLAN. This scheme with minimum transmission power resolves the hidden terminal problem. Propagation loss of received signal from associated communication pair is used for the proper values of transmission power and carrier sense level. Furthermore, adjusting these parameters destination by destination can reduce exposed terminals effectively. We evaluate the performance of the proposed scheme in residential building scenario with three criteria, aggregate throughput, fairness and frame error rate. Simulation results show that the proposed scheme can improve aggregate downlink throughput and fairness compared to previously proposed method that carrier sense level is set based on expected RSSI level of received packet from communicating pair. Furthermore, improvement of frame loss rate implies that the hidden terminal problem can be solved by the proposed scheme.

© 2018 IEEE. Emergency Rescue Information Sharing System gathers information about victims and provides it. In disaster, usable bandwidth is not enough and there are many requests for communication. If information about victims is continuously transmitted to a link whose bandwidth is compressed, congestion of the link occurs, causing delay or loss of information. Therefore, in order to prevent information loss and delay, transmitting information in consideration of the bandwidth of wireless links is important. In this poster, we propose a congestion control method by adjusting transmission speed of information on victims and load balancing to each wireless link.

© 2018 IEEE. In mobile ad hoc networks (MANET), reputation systems and price-based systems have been proposed. These systems require management systems for trust and reward. Due to the characteristics of MANET, the management systems are required to securely manage reputation and reward in a distributed manner. In this paper, we propose a Distributed Management system for Trust and Reward in MANET, called DMTR. DMTR can effectively detect uncooperative nodes which drop packets illegally. DMTR is utilising a blockchain which is a technique to manage virtual credit in peer-to-peer (P2P) networks. DMTR sets 'mining nodes' which play a role of managing the blockchain and introduces cooperative mining to accelerate settlement speed while keeping security. We confirm the efficiency of DMTR in network performances by simulation.

A maneuver coordination service (MCS) is a generic cooperative driving method for connected and automated vehicles. In an MCS, each vehicle periodically broadcasts its planned trajectory by vehicular communication. When a vehicle wants to change its planned trajectory, it can send a desired trajectory and initiate negotiation with surrounding vehicles. A vehicle that receives the desired trajectory decides whether to accept the negotiation and updates its planned trajectory so as not to conflict with the desired trajectory for coordination. Most previous works evaluated MCSs assuming that the number of vehicles is small and that negotiation is always accepted. However, in a congested environment, accepting all negotiations may not be optimal for traffic efficiency because accepting negotiation affects both the negotiation participants and the surrounding vehicles that are not involved in the negotiation. In this paper, we propose a negotiation acceptance scheme in maneuver coordination for efficient traffic. This paper also proposes a utility function that takes the negotiation effect into account. In addition, the agreement phase is incorporated into the negotiation protocol to maintain acceptance consistency among several negotiation participants. Simulation results show that making appropriate settings for the negotiation acceptance scheme improves traffic efficiency in congested environments.

Adverse events after surgery not only affect the patient's recovery but also increase the burden on doctors and patients due to prolonged hospitalization. Predicting adverse events from patient data before surgery with a machine learning method is highly expected. It is difficult to collect a large amount of patient data since the number of surgeries in a year is limited and predict the occurrence of adverse events accurately since patient data are imbalanced data. To improve the accuracy of adverse event prediction, this paper proposes data generation with Filtered VAE for the preoperative prediction of adverse events. Filtered VAE has filters by the reconstruction error and by a machine learning method. After VAE generates minority class data, the two layers of filtering are used to remove low-quality minority class data that have little contribution to the adverse event prediction. In the evaluations, patient data obtained from Tokyo Dental University Ichikawa General Hospital were used. The proposed method can predict adverse events with a recall of 0.848, which is 5.6% more accurate than existing methods. The effects of filtering in Filtered VAE are visualized, and the reasons for the improvement in prediction accuracy are clarified. Furthermore, this paper shows that VAE can generate arbitrary patient data even in table data, corresponding to the distribution of the original patient data.

In this paper, we propose adaptive message prioritization for vehicular cooperative perception at target intervals. The purpose of this paper is to ensure that Connected and Autonomous Vehicles (CAVs) can perceive the surrounding traffic environment by exchanging wireless messages such as Cooperative Awareness Messages (CAMs) and Cooperative Perception Messages (CPMs). Especially, under challenging conditions such as the wireless congestion control or the mixed-traffic of CAVs and human-driven vehicles, the number of perceptible objects by these messages decreases due to the message transmission rate control under the congestion control or the less message transmission under the low CAV penetration rate. To perceive the surrounding objects under the challenging conditions, the proposed method assigns high transmission priority to CPMs that include the information of multiple objects perceived by in-vehicle sensors. Specifically, the method controls the transmission frequency of CAMs and CPMs by assigning transmission priority to them such that the maximum perception interval approaches the target interval. Simulation result shows that 75% CAVs perceive surrounding vehicles within the target interval even under both the wireless congestion control and 20% CAV penetration rate.

© 2019, Springer Nature Switzerland AG. In intelligent transportation systems (ITS), researches aimed to alleviate congestion and improve traffic flow are being conducted. This paper proposes intersection traffic control method considering remaining distance to destination and congestion of next road segment (ICDIC) for the purpose of improving the traffic capacity of the entire areas. In ICDIC, each vehicle transmits information about the remaining travel distance to the final destination and the next driving lane to roadside unit (RSU) installed at the intersection of the destination. The RSU classifies the vehicles for each driving lane based on the received information. The RSU calculates intersection traffic priority calculation for each vehicle based on the length of the remaining travel distance and the low congestion degree of the destination lane, and the priority of each lane are calculated by taking the average for each lane for each lane. For these processes, traffic capacity can be improved throughout the area.

Recently, vehicle-to-infrastructure (V2I) communication-based scheduling schemes for connected and automated vehicles (CAVs) on highway on-ramps have received attention because the schemes assign optimal merging times to CAVs based on their criteria, such as first-in first-out order or minimizing CAV total travel time. However, these schemes cause traffic congestion due to the criteria when the vehicle inflow traffic becomes high. To mitigate traffic congestion, a scheduling scheme should assign merging times to CAVs to decrease road vehicle density. In this paper, we propose a scheduling scheme for autonomous vehicle highway merging with an outflow traffic and fairness analysis. We formulate a multiobjective function that achieves both outflow traffic maximization and fairness in assigned merging times. Since more vehicles pass a highway on-ramp as highway on-ramp outflow traffic increases, the proposed scheme decreases vehicle density on roads by maximizing the outflow traffic. Furthermore, the proposed scheme distributes the vehicle density on both the main and ramp roads by fairly assigning their merging times. Simulation results show that the proposed scheme mitigates the potential traffic congestion of previous scheduling schemes. Furthermore, the proposed scheme improves outflow traffic from highway on-ramps by more than 200 veh/h compared with the previous scheduling schemes.