In this paper, we describe the current situation of the motorcycle industry, the world's markets, and the trend of motorcycle researches. The purpose is to promote researches and developments of motorcycles so that motorcycles become safer and more convenient, and people will obtain good user-experiences (UX) with their motorcycle in their life. We summarize the current research issues and the current social issues about motorcycles, and then we introduce a solution for them with informatics and its related science and engineering. To make a motorcycle safer and to enhance its mobility, it is essential to investigate the motorcycle dynamics. The motion of a rider is also essential, and it affects the motorcycle motion, whereas the motion of a car driver seldom affects the car motion. It is because the weight of a rider is large enough, comparing to the weight of a motorcycle, and a rider usually moves widely to operate the motorcycle. In order to investigate the dynamics of the motorcycle system, which consists of a motorcycle itself and a rider, it is required to obtain appropriate sensing data of both the motorcycle and the rider to improve the knowledge of the dynamics by the data. Such data and knowledge can be applied to other applications and services such as sensing road traffic conditions. In this paper, we introduce the concept of the research project, Bikeinformatics, and the capability of GNSS precise positioning to append adequate labels to measured data with low-cost sensors.
Monitoring mental health has received considerable attention as a countermeasure against the increasing occurrence of mental illness worldwide. However, current monitoring services incur costs because users are required to attach wearable devices or answer questions. To reduce such costs, many studies have used smartphone-based passive sensing technology to capture a user's mental state. This paper reviews those studies from the perspective of machine learning and statistical analysis. Forty-four studies published since 2011 have been reviewed and summarized from three perspectives: designed features, machine learning algorithm, and evaluation method. The features considered include location and mobility, activity, speech, sleep, phone usage, and context features. Tasks are classified as correlation analysis, regression tasks, and classification tasks. The machine learning algorithm used for each task is summarized. Evaluation metrics and cross validation methods are also summarized. For those who are not necessarily machine learning experts, we aim to provide information on typical machine learning framework for smartphone-based mental state estimation. For experts in the field, we hope this review will be a helpful tool to check for potential omissions.
When multiple applications at two sites connected by a best-effort service network through gateway (GW) equipment communicate simultaneously, the receive rate of a high-priority application communication flow can be smaller than its necessary bandwidth (BW). In this paper, we refer to this problem as the deficit in bandwidth of a high-priority flow (DBHPF) problem. In order to handle this problem, we consider controlling the BW assigned to each flow based on the available bandwidth (ABW) estimated by the GW. The estimated ABW can be larger than the actual ABW due to the error in the estimation. Thus, the receive rate of a high-priority application can be smaller than its necessary BW, even if the actual ABW is larger than the necessary BW. In this paper, we propose a priority-based BW control method that estimates the receive rate of each flow using estimated ABW and related information, and mitigates the effect of the DBHPF problem by controlling the transmission BW of each flow in order to compensate for the difference between the estimated receive rate and the necessary BW according to the priorities of flows. We call the proposed method estimated-receive-rate-based bandwidth control (eR2BC). We also propose a method for ABW estimation with less overhead than existing methods. We conducted experiments using the proposed methods in a virtual network constructed with virtual machines and confirmed that the proposed methods can mitigate the effect of the DBHPF problem better than existing methods.
Building structure information is essential for achieving various indoor location-based services (ILBSs). Our approach integrates a large amount of pedestrian trajectories acquired by pedestrian dead reckoning (PDR) for generating a pedestrian network structure. To generate highly accurate pedestrian network structures, the accuracy of each trajectory must be improved. In this paper, we propose a method to improve the accuracy of indoor PDR trajectories by using many such trajectories. First, we select reliable trajectories based on the stability of the sensing data. Next by analyzing the trend of the step lengths, we correct the length of the trajectories. Finally, with same-route trajectories, we generate average trajectories for each route. We experimentally used HASC-IPSC and found that our proposed method improved the accuracy of the trajectories. The cumulative error rate of the original pedestrian trajectories was 0.1111m/s. After adapting our proposed method, the rate improved to 0.0622m/s.
In this paper, we propose FlowScan: a pedestrian flow estimation technique based on a dashboard camera. Grasping flows of people is important for various purposes such as city planning and event detection. FlowScan can estimate pedestrian flows on sidewalks without taking much cost. Currently, dashboard cameras have been becoming so popular for preserving the evidence of traffic accidents and security reasons. FlowScan assumes that an application which analyzes video from the camera is installed on an on-board device. To realize such an application, we need to design a method for pedestrian recognition and occlusion-proof tracking of pedestrians. For pedestrian recognition, the application uses Deep Learning-based techniques; CNN (Convolutional Neural Networks) and LSTM (Long-Short-Term-Memory). In this process, the faces and backs of their heads are searched in the video separately to detect not only the number of pedestrians but also their directions. Then, a series of detected positions of heads are arranged into tracks depending on the similarity of locations and colors considering the knowledge about the movement of the vehicle and pedestrians. We have evaluated FlowScan using real video data recorded by a dashboard camera. The mean absolute error rate for people flow estimation of both directions was 18.5%, highlighting its effectiveness compared with the state-of-the-art.
This paper proposes cumulative sum detection, which can detect cyberattacks on Controller Area Network (CAN). Well-known existing attack detection techniques cause false positives and false negatives when there are long delays or early arrivals involving usual periodic message reception. The proposed technique can detect attacks with almost no false positives or false negatives, that is highly accurate even when there are a long delays or early arrivals. This paper evaluates the detection accuracy of existing techniques and the proposed technique using computer simulation with CAN data obtained from actual vehicles. By considering the evaluation result and the ease of parameter adjustment, we show that the cumulative sum detection is the best of these techniques.
In order to utilize renewable energy effectively, the generated surplus energy should be stored in batteries, and transferred to distant places with high demand in a microgrid. As a scalable mechanism for such energy transfer (energy interchange), we proposed an autonomous decentralized mechanism (ADM) based on Markov Chain Monte Carlo (MCMC), and clarified that our ADM accomplishes the global objective to quickly supply energy appropriately for energy demand all over the microgrid. In this paper, toward a resilient microgrid, we propose a method of directional energy interchange used in our ADM. We first design a method of the directional energy interchange to be able to quickly transfer energy in an appropriate direction on the basis of the advection-diffusion equation used in physics. Then, we investigate the performance of the proposed method through a simulation experiment considering energy shortage and emergency situations. Simulation results show that the proposed method (a) can quickly supply energy from a traditional centralized grid to a microgrid under energy shortage situations, and (b) can quickly gather distributed energy to a specific place (e.g., safe shelter) under emergency situations.