Delivery of medical multimedia contents through the TCP/IP network using RealSystem

We developed a low cost, user-friendly multimedia delivery system, to provide medical lectures saved as multimedia contents to persons engaged in medicine. This system was created using the RealSystem package with the TCP/IP network. Users can review lectures and medical meeting presentations with video and audio through the Internet, whenever convenient. Each medical source of video and slide has been clearly displayed on a screen. Members of medical associations or medical students can easily review the most interesting parts of these files. This system is being used efficiently in distance learning and aids the diffusion of the latest information and technology to busy physicians and medical students.     

Lane-change detection using a computational driver model

OBJECTIVE: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. METHOD: Using a "model tracing" methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. RESULTS: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. CONCLUSION: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. APPLICATION: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.     

Dynamic compensation by fusing a high-speed actuator and high-speed visual feedback with its application to fast peg-and-hole alignment

This paper presents a dynamic compensation concept to grapple with the dynamic defects of a traditional robot arm, especially while performing high-speed endpoint regulations. The proposed high-speed dynamic compensation concept offers a new point of view for cooperating with a traditional manipulator to realize highly dexterous performance of manipulations. The concept is realized through adoption of a high-speed light-weight actuator as well as endpoint closed loop configured high-speed cameras. The dynamic compensation is analyzed experimentally with 1000 Hz visual feedback and a high-speed finger for a robot arm in the case of one degree of freedom. The advantage of the proposed approach is that the modeling for the robot system’s dynamics is not needed, whereas it is necessary and trivial in order to realize high-speed regulations by traditional approaches. Thus, the control issue becomes easier with the proposed approach. As an application for this concept, fast peg-and-hole alignment with large position and attitude uncertainty is studied. The alignment algorithm is based on a visual compliance strategy. Alignment experiments show that with the proposed concept of dynamic compensation as well as visual compliant motion control, robust and fast convergence was realized for most cases.     

Multi-modal Information Integration for Interactive Multi-agent Systems

This paper proposes a multi-agent system that carries out cooperative work. To achieve this, we use Fuzzy Associative Memory Organizing Unit Systems (FAMOUS) and Conceptual Fuzzy Sets (CFS). By using these proposed methods, each agent robot can decide its own behaviour to suit the situation in its environment. We apply this system to an Intelligent Transportation System (ITS) and do simulations.     

Dynamic analysis and optimum design for control of beams with time-dependent changes of length

Structural systems with longitudinal mass flow can be seen in belt-conveyers, running magnetic tapes and flexible manipulators, etc. Transverse vibration problems of flexible longitudinally extensible beams are considered as a special class of vibration problems of structural systems with longitudinal mass flow. The optimum design problem to control the dynamic response of such structural systems is very important from engineering and practical viewpoints, but until now it has not been the subject of much study.Based on the method of dynamic response analysis of extensible beams presented in the authors' previous studies, a method of sensitivity analysis of dynamic response is presented in this paper. Utilizing the obtained sensitivities of the response with respect to design parameters, such as mass flow velocity, tension and rigidity etc., a new optimum design method to control the responses of such structural systems is also presented.     

The species fitness method for the evolution of cooperative behavior in a group task

This paper considers the evolution of cooperative behaviors as the interaction among agents using a genetic algorithm to improve the performance of the task in a group (group performance). Previous research often usedthe group fitness method, which evaluates group performance for the evolution of multiple groups in parallel. However, this entails large simulation costs and the evolution speed is slow.The individual fitness method that evaluates theindividual performance of the task entails a smaller simulation cost. However, it can not improve the group performance since each agent behaves selfishly. To optimize the group performance, it is important to include bothcompetition andsharing. Therefore, this paper presentsthe species fitness method, which shares the individual performances of agents belonging to the same species in a group that all have the same chromosomes. We show comparative experiments on these three methods on the evolutionary simulation of a foraging task in a group. To test the interaction among the agents, four kinds of species are evolved which show their communication ability by demonstrating whether the agent can send or receive the signal for food. Experimental results show that evaluating the species variance fitness leads the agents into reciprocative actions. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

A content search system considering the activity and context of a mobile user

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user’s activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user’s degree of concentration on his or her mobile device’s display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user’s activity and context, with the goal of reducing the user’s operation load for content search. The proposed system switches between two kinds of content search systems according to the user’s activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.     

Construction of living cellular automata using the Physarum plasmodium

The plasmodium of Physarum polycephalum is a unicellular and multinuclear giant amoeba that has an amorphous cell body. To clearly observe how the plasmodium makes decisions in its motile and exploratory behaviours, we developed a new experimental system to pseudo-discretize the motility of the organism. In our experimental space that has agar surfaces arranged in a two-dimensional lattice, the continuous and omnidirectional movement of the plasmodium was limited to the stepwise one, and the direction of the locomotion was also limited to four neighbours. In such an experimental system, a cellular automata-like system was constructed using the living cell. We further analysed the exploratory behaviours of the plasmodium by duplicating the experimental results in the simulation models of cellular automata. As a result, it was revealed that the behaviours of the plasmodium are not reproduced by only local state transition rules; and for the reproduction, a kind of historical rule setting is needed.     

Data-efficient learning of robotic clothing assistance using Bayesian Gaussian process latent variable model

ABSTRACT

Motor-skill learning for complex robotic tasks is a challenging problem due to the high task variability. Robotic clothing assistance is one such challenging problem that can greatly improve the quality-of-life for the elderly and disabled. In this study, we propose a data-efficient representation to encode task-specific motor-skills of the robot using Bayesian nonparametric latent variable models. The effectivity of the proposed motor-skill representation is demonstrated in two ways: (1) through a real-time controller that can be used as a tool for learning from demonstration to impart novel skills to the robot and (2) by demonstrating that policy search reinforcement learning in such a task-specific latent space outperforms learning in the high-dimensional joint configuration space of the robot. We implement our proposed framework in a practical setting with a dual-arm robot performing clothing assistance tasks.