A smart community utilizes information technology to interconnect and manage community infrastructures. Smart community networks should support a large number of Internet of Things (IoT) devices in community infrastructures to provide services such as smart grids and health monitoring systems. In comparison to cloud-based solutions, smart community services can be deployed in the edge computing area to reduce service latency and to encapsulate private and local information. Furthermore, smart community services can leverage network virtualization technologies to support IoT network services at the edge. A service-oriented container-based solution that processes data streams from IoT sensors using conventional hardware will improve the compatibility and latency of these virtualized network services at the edge. To this end, a software-based edge computing node, namely, the smart community edge (SCE), was proposed to develop a platform for smart community services. SCE supports data-tapping applications, especially for IoT devices, and has a stream processing feature with a comparatively shorter processing delay. This tapping and processing function was named multi-service authorized stream content analysis. SCE captures network stream data and enables service applications using shared memory buffers for a shorter processing delay. SCE supports services as Docker containers to provide remote deployment, service compatibility, and service isolation. SCE allows IoT services to run at the edge through conventional hardware devices, thus, reducing the service latency for delay-sensitive services, which approximately require to sustain latency less than 10 ms. The proposed SCE achieves 10 Gbps bandwidth with a 16 core server when compared to the f-stack library with a 5 Gbps bandwidth. SCE deployment on conventional hardware devices shows its capability of operating at 1-10 Gbps line rates to support up to eight services at 500 Mbps data bandwidth per service, while keeping the overall latency below 1 ms. Therefore, SCE provides a platform for delay-sensitive IoT services at the network edge.
In order to help people with disabilities of the upper extremities drink and eat, a simulator for self-feeding assistive robotic arm using solving method of inverse kinematics has been developed in this paper. Using inverse kinematics equations of 7 degrees of freedom (DOF) with an arm angle parameter, the robotic arm model was implemented in the simulator and its controller with mouse device was also designed. The arm angle parameter defined the angle between the arm plane and a reference plane was used to resolve the robotic arm redundancy. Three able-bodied persons participated in simulation experiments. They manipulated the robotic arm model in a virtual space using mouse device. It was found from the simulation results they successfully performed the drinking and eating tasks in our simulator.
Information processing in the brain is performed by the interactions of numerous number of neurons. However, much remains unknown about complex behaviors of a network composed of numerous number of neurons. Especially, in what case cross frequency coupling emerges and how it alters timings of individual firing are open questions. Therefore, in order to quantitatively evaluate the dynamics of the nervous group, we reproduced phenomena such as cross frequency coupling observed in the brain on a mathematical model and analyzed. The analyses of Fokker-Planck equation provides the region of gamma/theta oscillations as well as their cross frequency couplings. In addition, we demonstrated that the populational oscillation synchronizes sequences of spike trains, which is supposed to be a possible mechanism of memory coding in hippocampus by theta-gamma neural code.
In this paper, we propose a novel edge-type MPC which can be implemented on generic programmable logic controllers (PLCs) compliant to IEC61131-3 standard. The proposed method is composed of offline design and online control. The offline design is based on formula manipulation and the online control is based on a prediction filter with a plant model. The method can be applied to discretized-input cases such as on and off switching. We evaluated the method for an illustrative example of a chemical reactor. We show that real-time MPC on a generic PLC can be achieved via the example.
In model-based development in the automotive industry, functional tests are performed using models made by CAE tools. The model needs to reproduce some physical properties of the target system correctly. The previous research of the current authors proposes energy balanced based verification (EBBV) that verifies the correctness of the model focused on the energy conservation law. EBBV confirms the balance of input and output energy of the model from output information of the model. In this paper, we develop three tools to automate EBBV processes. Also, we make a modeling guideline suitable for EBBV, which is composed of tagging and a hierarchical model structure. Through numerical experiments, we check that three tools automate EBBV processes for models according to the modeling guideline. The target model of the experiments is a mild hybrid electric vehicle model made by MATLAB/Simulink, MapleSim, and CarMaker.
Periodic linear quadratic integrator control is applied to the individual blade pitch angle control of floating offshore turbine to reduce fatigue load as well as to improve power output. A methodology for selecting weighting matrices of optimal control is discussed in detail; in particular, the optimization of the phase parameter in the periodic input weighting matrix results in synchronization with the operating point of blade flapwise bending mode. The proposed method is applied to the NREL 5 MW wind turbine model and shows the reduction in blade flapwise bending compared with the averaged linear quadratic integrator.
In this paper, we propose the method for efficient real-time estimation of the odometry using a rotating 2D laser scanner and a scan-matching method. Our scan-matching method uses the odometry model, constituted 9-DoF parameters of positions, postures and linear velocities. And all parameters are estimated by minimizing the cost-function obtained distances between point clouds to surfels. In the experiment, we evaluated the accuracy of our method when the robot passes on a rough terrain space and a foresty work road. As result, The maximum position error is 0.19 m of 30 m distance traveled, compared with the RTK-GNSS output in a rough terrain space. And more highly accurately than a conventional method in a foresty work road.
A spectacular diversity of fishes under a crystal clear seawater in Okinawa attracts numerous scuba divers, snorkelers around the world. With the advancement in computer vision and deep learning, object detection is much more reliable than ever and find its application almost in every industry, and also in marine leisure activity. Being able to detect and recognize all underwater objects provides both an educational and amazing experience to divers and snorkelers to explore the underworld. However, it requires a system that could work in real-time with high accuracy. This is a challenge that all deep learning-based object detection algorithm is facing since there is a trade-off between time and accuracy. YOLOv3 is one of the fastest object detection algorithms that can work in real-time. We use this to train and test on our custom dataset. We collected the underwater fish image and built our dataset that contains 3548 images. We provide a comparative analysis of the training and evaluation of three different datasets. With data augmentation, our model can achieve up to 92% of mAP, and we also show what role that negative data impact the performance of the model.
In order to improve programming education in C language, it is important to develop an environment that is easy for beginners to learn. At the same time, a function that can support the instructor is also essential, such as monitoring learners and collecting learning logs. In this paper, we develop a programming learning support system for beginners of C language to achieve the three objectives: (1) providing a function for beginners to make them concentrate on essential learning intended by the instructor, (2) providing a function to collect the log data on the learning process for realizing the analysis required by the instructor, and (3) providing a function to realize presentation and collection of learning tasks commonly performed in C language education, and to support these evaluations and counting. This paper explains the concept and function of the proposed system in detail, and to evaluate the usefulness of the proposed system from the viewpoints of both the learners and its instructors through the practical use and the experiments.