29450095
Cognitive models of the brain
The human brain is the most complicated organ in the universe and a new frontier yet to be explored in an interdisciplinary approach. Investigation of the brain is a unique problem that requires recursive mental power to explore the brain using the brain. This paper attempts to develop functional and cognitive models of the brain by using cognitive informatics and formal methodologies. This paper adopts a memory-based approach to explore the brain, and to demonstrate that memory is the foundation for any natural intelligence. Structures of memories are explored and cognitive models of the natural intelligence are proposed. Cognitive mechanisms of the brain, including hypotheses and theories on the thinking engine of the brain, long-term memory establishment, and roles of sleep in long-term memory development, are investigated. The models and theories are applied to explain a number of fundamental physiological and psychological phenomena.
21184887
The cognitive processes of abstraction and formal inferences
Theoretical research is predominately an inductive process; while applied research is mainly a deductive process. Both inference processes are based on the cognitive process and means of abstraction. This paper describes the cognitive processes of abstraction and formal inferences such as deduction, induction, abduction, and analogy. The hierarchy of abstraction and the descriptivity of abstract means at different levels are analyzed. A set of mathematical models of formal inference methodologies are developed. Formal descriptions of the five cognitive processes of abstraction and inferences are presented using real-time process algebra (RTPA). Applications of abstraction and formal inferences in dealing with complicated problems in large-scale software system development in software engineering are discussed.
18101194
The Real-Time Process Algebra (RTPA)
The real-time process algebra (RTPA) is a set of new mathematical notations for formally describing system architectures, and static and dynamic behaviors. It is recognized that the specification of software behaviors is a three-dimensional problem known as: (i) mathematical operations, (ii) event/process timing, and (iii) memory manipulations. Conventional formal methods in software engineering were designed to describe the 1-D (type (i)) or 2-D (types (i) and (iii)) static behaviors of software systems via logic, set and type theories. However, they are inadequate to address the 3-D problems in real-time systems. A new notation system that is capable to describe and specify the 3-D real-time behaviors, the real-time process algebra (RTPA), is developed in this paper to meet the fundamental requirements in software engineering.RTPA is designed as a coherent software engineering notation system and a formal engineering method for addressing the 3-D problems in software system specification, refinement, and implementation, particularly for real-time and embedded systems. In this paper, the RTPA meta-processes, algebraic relations, system architectural notations, and a set of fundamental primary and abstract data types are described. On the basis of the RTPA notations, a system specification method and a refinement scheme of RTPA are developed. Then, a case study on a telephone switching system is provided, which demonstrates the expressive power of RTPA on formal specification of both software system architectures and behaviors. RTPA elicits and models 32 algebraic notations, which are the common core of existing formal methods and modern programming languages. The extremely small set of formal notations has been proven sufficient for modeling and specifying real-time systems, their architecture, and static/dynamic behaviors in real-world software engineering environment.
7087864
Formal description of the cognitive process of decision making
Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a set of alternatives based on certain criteria. Decision theories are widely applied in a number of disciplines encompassing cognitive science, computer science, management science, economics, sociology, psychology, political science, and statistics. The studies on decision making can be categorized into two classes: descriptive and normative theories. A number of decision strategies have been proposed from different angles and application domains such as the maximum expected utility and Bayesian method. However, there is still a lack of a fundamental and mathematical decision model and a rigorous cognitive process for decision making. This paper presents a decision making process on the basis of the layered reference model of the brain (LRMB). The cognitive process of decision making is modeled as a sequence of Cartesian-product based selections. A rigorous description of the decision process in real-time process algebra (RTPA) is presented. Different decision making strategies are comparatively analyzed. The result shows these strategies can be well fit in the formally described decision process. The cognitive process of decision making may be applied in a wide range of decision-based systems, such as cognitive informatics, software agent systems, expert systems, and decision support systems.
19029975
Design and Implementation of an Automatic RTPA Code Generator
Real-time process algebra (RTPA) is a mathematics-based notation system for the specification and refinement of realtime and safety-critical systems. This paper presents the work on designing and developing a set of tools that facilitate automatic generation of C++ code from RTPA specifications of system architectures and behaviors. A two-phrase strategy has been employed in the design of the toolkit. In the first phrase, an RTPA specification is lexically and syntactically analyzed and type-checked, which results in a set of abstract syntax trees. In the second phrase, C++ code is generated using the RTPA-to-C++ mapping strategies and patterns when walking down the abstract syntax trees. The RTPA-to-C++ mapping strategies specify both the traditional sequential part of RTPA mapping onto the standard C++ library and the real-time features of RTPA, such as interrupt, concurrency, duration, and event/time-driven dispatch, mapping onto the RTPA run-time library, which provides real-time support and is implemented using real-time kernel techniques. The toolkit implemented includes an RTPA lexer, an RTPA parser, an RTPA type-checker, and an RTPA code-generator. The experimental results show that RTPA specifications can be rigorously checked and corresponding C++ code can be automatically generated from RTPA specifications using the toolkit. The automatically generated code is executable and effective under the support of the standard C++ library and the specially developed RTPA run-time library
44372041
Software Engineering Process Benchmarking
This paper proposes a Software Engineering Process (SEP) benchmarking methodology and a benchmark-gap analysis technique to assist industrial practitioners in quantitative software engineering. This work adopts the comprehensive SEP Assessment model (SEPRM), as a foundation to SEP benchmarking. In this approach, a number of conventional benchmarking challenges may be overcome. Case studies are presented to demonstrate the usage of the benchmarking technologies and supporting tools.
37194320
The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty
Abstract The evidential reasoning (ER) approach is a method for multiple attribute decision analysis (MADA) under uncertainties. It improves the insightfulness and rationality of a decision making process by using a belief decision matrix (BDM) for problem modelling and the Dempster–Shafer (D–S) theory of evidence for attribute aggregation. The D–S theory provides scope and flexibility to deal with interval uncertainties or local ignorance in decision analysis, which is not explored in the original ER approach and will be investigated in this paper. Firstly, interval uncertainty will be defined and modelled in the ER framework. Then, an extended ER algorithm, IER, is derived, which enables the ER approach to deal with interval uncertainty in assessing alternatives on an attribute. It is proved that the original ER algorithm is a special case of the IER algorithm. The latter is demonstrated using numerical examples.
44707452
Diagnosis of clustered faults for identical degree topologies
Huang et al, [1998] studied the diagnosis of clustered faults and wafer testing. They proposed a diagnosis algorithm for a probabilistic fault model in simple rectangular grid structures. In this paper, we extend their results and study the diagnosis algorithm for arbitrary identical degree topologies. Our results are useful and valid for a large class of topologies. We investigate the local and global performance of the algorithm under several important fault distributions: Bernoulli failure distribution, Gamma failure distribution, and exponential failure distribution. We demonstrate that the diagnosis scheme can identify almost all nodes successfully even if the percentage of fault-free units is low (much lower than 50%) while almost all units are guaranteed to be correctly identified. In addition, we show that the performance of the algorithm is insensitive to the changes of the percentage.
13993519
Interval efficiency assessment using data envelopment analysis
This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input-output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and @a-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.
20180608
An Image Analysis System for Tongue Diagnosis in Traditional Chinese Medicine
We introduced a computer-aider tongue examination system, which can reduce the large variation between the diagnosis results of different doctors and quantize the tongue properties automatically in traditional Chinese medicine (TCM) diagnosis. Several key issues and algorithms in this system are discussed, involving some newly proposed image analysis techniques: i) a new tongue color calibration scheme is proposed to overcome the drawbacks of the previous method; ii) a gradient vector flow (GVF) snake based model integrating the chromatic information of the tongue image is used to extract the tongue body; iii) an unsupervised segmentation method of color-texture regions is adopted to quantitatively analyze the distribution of the substance and the coat on the tongue surface. The experimental results show that the presented system has a great deal of potential for the tongue diagnosis.
793367
3D Brain surface matching based on geodesics and local geometry
The non-rigid registration of surfaces is a complex and difficult task for which there are many important applications, such as comparing shape between deformable objects and comparing associated function. This paper presents a new approach for brain surface matching by determining the correspondence of 3D point sets between pairs of surfaces. The algorithm is based on shape using a combination of geodesic distance and surface curvature. There are two major procedures involved. An initial sparse set of corresponding points is first generated by matching local geometrical features. Geodesic distance interpolation is then employed hierarchically in order to capture the complex surface. By this scheme, surface correspondence and triangulation are computed simultaneously. Experiments applied to human cerebral cortical surfaces are shown to evaluate the approach. It is shown that the proposed method performs well for both surface matching and surface shape recovery.