Sitting Posture Recognition and Location Estimation for Human-Aware Environment

Realization of human-computer symbiosis is an important idea in the context of ubiquitous computing. Symbiotic Computing is a concept that bridges the gap between situations in Real Space (RS) and data in Digital Space (DS). The main purpose is to develop an intelligent software application as well as establish the next generation information platform to develop the symbiotic system. In this paper, the authors argue that it is necessary to build ’Mutual Cognition’ between human and system. Mutual cognition consists of two functions: ’RS Cognition’ and ’DS Cognition’. This paper examines RS Cognition, which consists of many software functions for perceiving various situations like events or humans’ activities in RS. The authors develop two perceptual functions, sitting posture recognition and human’s location estimation for a person, as RS perception tasks. In the resulting experiments, developed functions are quite competent to recognize a human’s activities.

The Formal Design Model of a File Management System (FMS)

Files are a typical abstract data type for data objects and software modeling, which provides a standard encapsulation and access interface for manipulating large-volume information and persistent data. File management systems are an indispensable component of operating systems and real-time systems for file manipulations. This paper develops a comprehensive design pattern of files and a File Management System (FMS). A rigorous denotational mathematics, Real-Time Process Algebra (RTPA), is adopted, which allows both architectural and behavioral models of files and FMS to be rigorously designed and implemented in a top-down approach. The conceptual model, architectural model, and the static/dynamic behavioral models of files and FMS are systematically presented. This work has been applied in the design and modeling of a real-time operating system (RTOS+).

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Qualitative Reasoning Approach to a Driver’s Cognitive Mental Load

This paper explores applying qualitative reasoning to a driver’s mental state in real driving situations so as to develop a working load for intelligent transportation systems. The authors identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the driver’s cognitive state, the authors will measure eye movements during car-driving situations. Data can be acquired for the various actions of a car driver, in particular braking, acceleration, and steering angles from the experiment car. The authors constructed a driver cognitive mental load using the framework of qualitative reasoning. The response of the model was checked by qualitative simulation. The authors also verified the model using real data collected by driving an actual car. The results indicated that the model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.

Toward Automatic Answers in User-Interactive Question Answering Systems

A strategy of automatic answer retrieval for repeated or similar questions in user-interactive systems by employing semantic question patterns is proposed in this paper. The used semantic question pattern is a generalized representation of a group of questions with both similar structure and relevant semantics. Specifically, it consists of semantic annotations (or constraints) for the variable components in the pattern and hence enhances the semantic representation and greatly reduces the ambiguity of a question instance when asked by a user using such pattern. The proposed method consists of four major steps: structure processing, similar pattern matching and filtering, automatic pattern generation, question similarity evaluation and answer retrieval. Preliminary experiments in a real question answering system show a precision of more than 90% of the method.

Adaptive Study Design Through Semantic Association Rule Analysis

Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experiment shows promising results.

A Value-Based Framework for Software Evolutionary Testing

The fundamental objective in value-based software engineering is to integrate consistent stakeholder value propositions into the full extent of software engineering principles and practices so as to increase the value for software assets. In such a value-based setting, artifacts in software development such as requirement specifications, use cases, test cases, or defects, are not treated as equally important during the development process. Instead, they will be differentiated according to how much they are contributing, directly or indirectly, to the stakeholder value propositions. The higher the contributions, the more important the artifacts become. In turn, development activities involving more important artifacts should be given higher priorities and greater considerations in the development process. In this paper, a value-based framework is proposed for carrying out software evolutionary testing with a focus on test data generation through genetic algorithms. The proposed framework incorporates general principles in value-based software testing and makes it possible to prioritize testing decisions that are rooted in the stakeholder value propositions. It allows for a cost-effective way to fulfill most valuable testing objectives first and a graceful degradation when planned testing process has to be shortened.

Generic Cabling of Intelligent Buildings Based on Ant Colony Algorithm

Generic cabling is a key component for multiplex cable wiring. It is one of the basic foundations of intelligent buildings. Using operation flow in generic cabling, the index constraints affecting generic cabling have been evolved in this paper. A mathematical model is built based on the ant colony algorithm with multiple constraints, and improvements were made on the original basis to extend the ant colony algorithm from the regular simple ant colony and structure to a multi-ant colony and structure. The equilibrium settlement of multiplex wiring is realized according to the introduction of the multi-ant colony model. The ant cycle model is combined to extend the optimization target from the local wiring path to the entire wiring path, and to solve the drawbacks existing in the regular ant colony algorithm and other search algorithms that take the local wiring path as the optimization target. The introduced retrospective algorithm make the ants avoid the path marked “invalid” in the subsequent search process and improves the search performance and convergence speed of the ant colony algorithm.

Four-Channel Control Architectures for Bilateral and Multilateral Teleoperation

The four-channel architecture in teleoperation with force feedback has been studied in various existing literature. However, most of them focused on Lawrence architecture and did not research other cases. This paper proposes two other four-channel architectures: passive four-channel architecture and passive four-channel architecture with operator force. Furthermore, two types of multilateral shared control architecture based on passive four-channel architecture, which exists in space teleoperation, are put forward. One is dual-master multilateral shared control architecture, and the other is dual-slave multilateral shared control architecture. Simulations show that these four architectures can maintain stability in the presence of large time delay.

Petri Nets and Discrete Events Systems

This paper provides an overview over the relationship between Petri Nets and Discrete Event Systems as they have been proved as key factors in the cognitive processes of perception and memorization. In this sense, different aspects of encoding Petri Nets as Discrete Dynamical Systems that try to advance not only in the problem of reachability but also in the one of describing the periodicity of markings and their similarity, are revised. It is also provided a metric for the case of Non-bounded Petri Nets.