Critical situations such as running into an impasse and failure of decision-making during collaborative work are often hard to recognize by team members themselves. Such critical situations drive the collaborative work into poor continuity. Remote collaboration is even harder to stay active due to communication difficulty in decision-making. This study argues an effective visualization method for monitoring causal factors of conversational situation by multimodal dialogue analysis from turn-taking and mental workload for collaborative working and communication use. Our approach to Follow Awareness visualizes frequency of turn-taking and changing mental workload by the analysis of an EEG (electroencephalogram) based index. Mental workloads of two team members are compared as difference in their changes within a predetermined amount of times for the analysis of follows between the team members. Frequency of turn-taking and changes in mental workload are then displayed by a mobile device during their collaborative work for validation in real-time. Evaluation results from a case study of collaborative work in pair programming presented large difference of changes in mental workload at the point their active communication turned to silence by a small setback.

As a practical opportunity for educating Japanese young developers in the field of embedded software development, a software design contest involving the design of software to automatically control a line-trace robot, and conduct running performance tests was held. In this paper,we give the results of the contest from the viewpoint of software quality evaluation. We create a framework for evaluating the software quality which integrated design model quality and the final system performance, and conduct analysis using the framework. As a result of analysis,it is found that the quantitative measurement of the structural complexity of the design models bears a strong relationship to qualitative evaluation of the design conducted by judges. It is also found that there is no strong correlation between design model quality evaluated by the judges and the final system performance. For embedded software development, it is particularly important to estimate and verify reliability and performance in the early stages,using the model. Based on the analysis result,we consider possible remedies with respect to the models submitted,the evaluation methods used and the contest specifications. In order to adequately measure several non-functional quality characteristics including performance on the model,it is necessary to improve the way of developing robot software (such as applying model driven development)and reexamine the evaluation methods.

During this research, a CAI system prototype, by which the courseware of the computer networks can be learned, was developed. By developing this system, through the SCO presentation program based on SCORM, which uses the JavaScript and the JavaApplet, self-learning is possible even if the LMS client cannot connect to the LMS server. Furthermore, the SP-chart of arbitrary score and the caution indexes for the students can be presented from the database by the LMS system based on SCORM.

We report the results of dynamic measurements which evaluate the effectiveness of an application specific microprocessor for fuzzy control and fuzzy information processing. We propose to specialize an architecture of microprocessor for fuzzy theoretic operations using quantitative techniques developed by designers of reduced instruction set computer (RISC). In particular, an introduction of specialized instructions is considered. Experimental results show that we can achieve as high as 2.5 speed up of a program for fuzzy control by introducing two instructions-min and max.

Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy partition. The /spl delta/ rule, which is a basic learning method in neural networks, is used for parameter identification of a fuzzy model. SOFIA consists of four stages which effectively realize structure identification and parameter identification. The procedure of SOFIA is concretely demonstrated by a simple example which has been used in some modeling exercises. The identification result shows effectiveness of SOFIA. Next, the authors apply SOFIA to a prediction problem for CO concentration in the air at the busiest traffic intersection in a large city of Japan. Prediction results show that the fuzzy model is much better than a linear model. Furthermore, the authors simulate a control system for keeping CO concentration at a constant level by using the identified fuzzy model. A self-learning method for adaptively modifying controller parameters by /spl delta/ rule is introduced because the dynamics of real CO concentration system changes gradually over a long period of time. Two self-learning controllers are designed in this simulation. One is a self-learning linear PI controller. The other is a self-learning fuzzy PI controller. The authors investigate robustness and adaptability of this control system for disturbance and parameter perturbation of the CO concentration model. Simulation results show that the self-learning fuzzy controller is more robust and adaptive.>

We propose a framework to manage interrelated tasks concurrently and successively, and address the issues of event handling and task scheduling: the event handler organizes interrelated tasks from a static point of view, and the task scheduler controls them on the basis of the dynamic behavior properties. In order to design our flexible control mechanism, we introduce the state transition graph and AND-OR graph in the event handler, whereas in the task scheduler the timestamp ordering method is applied to. Additionally, we describe the failure handling, concerning the cooperation between the event handler and task scheduler.>

We have applied the discrimination analysis proposed by Norris, Pilsmorth, and Baldwin to the diagnosis of valvular heart diseases. They proposed the diagnosis method which uses concepts from fuzzy set theory. It consists of two independent parts: discrimination analysis and connectivity analysis. We performed the experiments in order to evaluate the effectiveness of the proposed discrimination analysis part of the method. Also, we extended the original method to handle partial manifestation of symptoms and severity of diseases by using fuzzy sets. In addition, we introduced the concept of prototypicalness of patients with a particular disease to improve the performance of the diagnosis. The results of the experiments are very promising. In the best case, we achieved a rate of true positive diagnosis of 81% while maintaining a rate of false positive diagnosis at the low level of 10%. We report the quantitative results of the experiments.>

Two types of computer boards including custom-designed VLSI chips have been developed to add a qualitative reasoning capability to the real-time control of autonomous mobile robots. The design and operation of these boards are first described, and an example of their use for the autonomous navigation of a mobile robot is presented. The development of qualitative reasoning schemes emulating human-like navigation in a priori unknown environments is discussed. The efficiency of such schemes, which can consist of as little as a dozen qualitative rules, is illustrated in experiments involving an autonomous mobile robot navigating on the basis of very sparse and inaccurate sensor data.>

An approach merging the concepts of fuzzy logic and sensor-based behaviors has been developed to add a qualitative reasoning capability to the real-time control of autonomous mobile robots. The approach is implemented using custom-designed computer boards which include recently developed VLSI fuzzy inferencing chips. The design and operation of these boards are first described and an example of their use for the autonomous navigation of a mobile robot is presented. The development of qualitative reasoning schemes emulating human- like navigation in a-priori unknown environments is discussed. The approach using superposition of elemental sensor-based behaviors expressed in the fizzy Sets theoretic framework is shown to allow easy development and testing of the inferencing rule base, while providing for progressive addition of behaviors to resolve situations of increasing complexity. The efficiency of such schemes, which can consist of as little as a dozen qualitative rules, is illustrated in experiments involving an autonomous mobile robot navigating on the basis of very sparse and inaccurate sensor data.

A general-purpose fuzzy logic inference engine for real-time control applications has been designed and fabricated in a 1- mu m, 3.3-V, DLM CMOS technology. Up to 102 rules are processed in parallel with a single 688 K transistor device. Features include a dynamically reconfigurable and cascadable architecture, a TTL-compatible host interface, laser programmed redundancy, a special mode for testability, RAM rule storage, and on-chip fuzzification and defuzzification.>

Flute is a heuristic floorplanner that operates as one of the agents of the Cadre - a system of cooperating expert agents for converting a hierarchical structural description into full custom VLSI layout. Flute is modeled on the human floorplanning process and uses a mixture of rule-based programming, state control and algorithmic operators. Initially, a topological plan is generated by placing modules on a grid graph. From this plan, a constraint graph is prepared. This graph is solved in order to add geometric size and placement information to the floorplan.

The role of inferencing with uncertainty is becoming more important in rule-based expert systems (ES), since knowledge given by a human expert is often uncertain or imprecise. We have succeeded in designing a VLSI chip which can perform an entire inference process based on fuzzy logic. The design of the VLSI fuzzy inference engine emphasizes simplicity, extensibility, and efficiency (operational speed and layout area). It is fabricated in 2.5 μm CMOS technology. The inference engine consists of three major components; a rule set memory, an inference processor, and a controller. In this implementation, a rule set memory is realized by a read only memory (ROM). The controller consists of two counters. In the inference processor, one data path is laid out for each rule. The number of the inference rule can be increased adding more data paths to the inference processor. All rules are executed in parallel, but each rule is processed serially. The logical structure of fuzzy inference proposed in the current paper maps nicely onto the VLSI structure.

This paper describes a new approach for IC layout and compaction. The compaction problem is translated into a mixed integer-linear programming problem of a very special form. A graph-based optimization algorithm is used to solve the resulting problem. An experimental program that uses the above techniques is described. The program could be used either as an aid to hand layout or as the bottom part of an automatic layout generation program.