7477567
Design formalism for collaborative assembly design
Abstract Joints in product design are common because of the limitations of component geometric configurations and material properties, and the requirements of inspection, accessibility, repair, and portability. Collaborative product design is emerging as a viable alternative to the traditional design process. The collaborative assembly design (AsD) methodologies are needed for distributed product development. Existing AsD methodologies have limitations in capturing the non-geometric aspects of designer's intent on joining and are not efficient for a collaborative design environment. This paper introduces an AsD formalism and associated AsD tools to capture joining relations and spatial relationship implications. This AsD formalism allows the joining relations to be modeled symbolically for computer interpretation, and the model can be used for inferring mathematical and physical implications. An AsD model generated from the AsD formalism is used to exchange AsD information transparently in a collaborative AsD environment. An assembly relation model and a generic assembly relationship diagram are to capture assembly and joining information concisely and persistently. As a demonstration, the developed AsD formalism and AsD tools are applied on a connector assembly with arc weld and rivet joints.
299693
Geometry-based semantic ID for persistent and interoperable reference in feature-based parametric modeling
In current feature-based parametric design systems, the reusability principle is not fully supported as it was expected. Unpredictability and ambiguity of models often happen during design modification within one system as well as among different systems. This reference deficiency significantly reduces the power of feature-based parametric modeling, where geometry re-evaluation generates unexpected shapes. In this paper, a sufficient condition of B-Rep variance based on geometry continuity in parametric complex Euclidean (P^pC^3) space is proposed. Shape and relation parameters are differentiated in P^pC^3, thus parametric family can be defined. A semantic id scheme based on continuity of geometry is developed to solve the problem of naming persistency and to improve interoperability of CAD feature modeling. Hierarchical namespaces localize entity creation and identification. All geometric and topological entities are referred uniformly based on surface ids, and topology semantics is retained in id itself.
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.
208933693
What are Textons?
Textons refer to fundamental micro-structures in natural images (and videos) and are considered as the atoms of pre-attentive human visual perception (Julesz, 1981). Unfortunately, the word "texton" remains a vague concept in the literature for lack of a good mathematical model. In this article, we first present a three-level generative image model for learning textons from texture images. In this model, an image is a superposition of a number of image bases selected from an over-complete dictionary including various Gabor and Laplacian of Gaussian functions at various locations, scales, and orientations. These image bases are, in turn, generated by a smaller number of texton elements, selected from a dictionary of textons. By analogy to the waveform-phoneme-word hierarchy in speech, the pixel-base-texton hierarchy presents an increasingly abstract visual description and leads to dimension reduction and variable decoupling. By fitting the generative model to observed images, we can learn the texton dictionary as parameters of the generative model. Then the paper proceeds to study the geometric, dynamic, and photometric structures of the texton representation by further extending the generative model to account for motion and illumination variations. (1) For the geometric structures, a texton consists of a number of image bases with deformable spatial configurations. The geometric structures are learned from static texture images. (2) For the dynamic structures, the motion of a texton is characterized by a Markov chain model in time which sometimes can switch geometric configurations during the movement. We call the moving textons as "motons". The dynamic models are learned using the trajectories of the textons inferred from video sequence. (3) For photometric structures, a texton represents the set of images of a 3D surface element under varying illuminations and is called a "lighton" in this paper. We adopt an illumination-cone representation where a lighton is a texton triplet. For a given light source, a lighton image is generated as a linear sum of the three texton bases. We present a sequence of experiments for learning the geometric, dynamic, and photometric structures from images and videos, and we also present some comparison studies with K-mean clustering, sparse coding, independent component analysis, and transformed component analysis. We shall discuss how general textons can be learned from generic natural images.
378133
LFM-Pro: a tool for detecting significant local structural sites in proteins
MOTIVATION
The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features.


RESULTS
We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features.


AVAILABILITY
The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.
21557809
Formal description of the cognitive process of problem solving
One of the fundamental human cognitive processes is problem solving. Most of the decisions we make relate to some kind of problems we try to solve no matter how trivial and critical the problem may be. The problem solving process entails performing in a new situation with information acquired and knowledge learned from past situations. As a higher level cognitive process, problem solving involves the correlation process effort to connect newly encounter problem object(s) with the object-attribute-relation (OAR) model representation of knowledge in the brain. The goal of problem solving is to search along various solution paths within the problem solver's knowledge base in the memory. When a problem object is identified, problem solving can be perceived as a search process in the memory space for finding a relationship between a set of problem-solving goals and a set of alternative paths. This paper presents a mathematical and cognitive model that describes problem solving as a cognitive process. The cognitive structures of the brain and the mechanisms of internal knowledge representation behind the cognitive process of problem solving are explained. The cognitive process is then formally and rigorously described using real-time process algebra (RTPA) base on the aforementioned models. Extended discussions are presented on applications of the cognitive process model of problem solving in software engineering and psychology.
46137989
On information and knowledge representation in the brain
The cognitive models of information representation and the capacity of human memory are fundamental research areas in cognitive informatics, which help to reveal the mechanism and potential of the brain. This paper develops the object-attribute-relation (OAR) model for describing information representation and storage in the brain. According to the OAR model, the human memory and knowledge are represented by relations, i.e. connections of synapses between neurons, rather than by the neurons themselves as the traditional container metaphor described. Based on the OAR model, the memory capacity of the human brain is calculated as in the order of 10/sup 8432/ bits. The determination of the magnitude of human memory capacity is not only theoretically significant in cognitive informatics, but also practically useful to estimate the human potential, as well as the gap between the natural and machine intelligence.