Sketch Of Mechanism Research Articles

Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation

Abstract This paper introduces a new method using deep neural networks for the interactive digital transformation and simulation of n-bar planar linkages, which consist of revolute and prismatic joints, based on hand-drawn sketches. Instead of relying solely on computer vision, our approach combines topological knowledge of linkage mechanisms with the outcomes of a convolutional deep neural network. This creates a framework for recognizing hand-drawn sketches. We generate a dataset of synthetic images that resemble hand-drawn sketches of linkage mechanisms. Next, we fine-tune a state-of-the-art deep neural network to detect discrete objects using building blocks that represent joints and links in various positions, sizes, and orientations within these sketches. We then conduct a topological analysis on the detected objects to construct a kinematic model of the sketched mechanisms. The results demonstrate the effectiveness of our algorithm in handling hand-drawn sketches and converting them into digital representations. This has practical implications for improving communication, analysis, organization, and classification of planar mechanisms.

How and when are topological explanations complete mechanistic explanations? The case of multilayer network models

The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019, https://doi.org/10.1080/09515089.2018.1512090) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and when topological explanations are mechanistic. First, if topological explanations were just mechanism sketches, this implies that they could be enriched by replacing topological terms with mechanistic detail. This, however, conflicts how topological explanations are used in scientific practice. Second, Zednik’s account fails to show how topological properties can be organizational properties of mechanisms that have a place in mechanistic explanation. The core issue is that Zednik’s account ignores that topological properties often are global properties while mechanistic explanantia refer to local properties. We demonstrate how these problems can be solved by a recent account of mechanistic completeness (Craver and Kaplan in Br J Philos Sci 71(1):287–319, 2020, https://doi.org/10.1093/bjps/axy015; Kohár and Krickel in Calzavarini and Viola (eds) Neural mechanisms—new challenges in the philosophy of neuroscience, Springer, New York, 2021, https://doi.org/10.1007/978-3-030-54092-0_17) and use a multilayer network model of Alzheimer’s Disease to illustrate this.

No computation without implementation? A potential problem for the single hierarchy view of physical computation

The so-called integration problem concerning mechanistic and computational explanation asks how they are related to each other. One approach is that a computational explanation is a species of mechanistic explanation. According to this view, computational or mathematical descriptions are mechanism sketches or macroscopic descriptions that include computationally relevant and exclude computationally irrelevant physical properties. Some suggest that this results in a so-called single hierarchy view of physical computation, where computational or mathematical properties sit together in the same mechanistic hierarchy with the implementational properties. This view can be contrasted with a separate hierarchy view, according to which computational and physical descriptions have their own hierarchies which are related to each other via a bridging implementation relation. The single hierarchy view has been criticized for downplaying the explanatory value of computational explanations and not being hospitable to multiple realization of cognitive processes. In this paper, I argue that (1) the aforementioned criticisms fail, and (2) there might be a deeper problem with the single hierarchy view, which is that the single hierarchy view might collapse into a separate hierarchy view. The kind of abstraction used by the single hierarchy view does not seem to grant mathematical or computational descriptions but only more stripped physical or implementational descriptions.

Psychology and Neuroscience: The Distinctness Question

In a recent paper, Gualtiero Piccinini and Carl Craver have argued that psychology is not distinct from neuroscience. Many have argued that Piccinini and Craver’s argument is unsuccessful. However, none of these authors have questioned the appropriateness of Piccinini and Craver’s argument for their key premise—that functional analyses are mechanism sketches. My first and main goal in this paper is to show that Piccinini and Craver offer normative considerations (on what functional analyses should be) in support of what is a descriptive premise and to provide some guidelines on how to argue for this premise. My second goal is to show that the distinctness question should be of great significance for philosophy of cognitive science.

Theory Development in Comparative Social Research

While questions of methodology and research design have received a lot of attention, less is known about theory development in comparative social research. As theoretical objectives and orientations are diverse, theorizing takes many forms, ranging from orienting statements and typologies to different kinds of causal propositions. After introducing different understandings of “theory” and associated types of research questions, the article discusses the interplay between empirical research and theory development in comparative social research. Using examples from different fields of application, I argue that theory development in comparative research can be hampered by placing too much emphasis on general micro-level theories, but also by a lack of theoretical abstraction, that intertwines mechanism sketches with historical and contextual details of the particular macro-level phenomena under investigation. The article calls for a greater focus on meso-level theorizing, as it has the greatest potential to produce theoretical knowledge about contextual variation in causal mechanisms and to motivate the development of theoretical models that are explicit enough to be systematically revised across studies.

Models and mechanisms in network neuroscience

ABSTRACTThis paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a long-standing research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation, and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are nonexplanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general.

Mechanisms and Model-Based Functional Magnetic Resonance Imaging

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored.

'Genetic Coding' Reconsidered: An Analysis of Actual Usage.

This article reconsiders the theoretical role of the genetic code. By drawing on published and unpublished sources from the 1950s, I analyse how the code metaphor was actually employed by the scientists who first promoted its use. The analysis shows that the term ‘code’ picked out mechanism sketches, consisting of more or less detailed descriptions of ordinary molecular components, processes, and structural properties of the mechanism of protein synthesis. The sketches provided how-possibly explanations for the ordering of amino acids by nucleic acids (the ‘coding problem’). I argue that employing the code metaphor was justified in virtue of its descriptive-denotational and explanatory roles, and because it highlighted a similarity with conventional codes that was particularly salient at the time. 1 Introduction2 Coding Schemes in the 1950s 2.1 The research problem: Determining amino acid sequences 2.2 The solution: Mapping schemes or ‘codes’ 3 The Code Metaphor Played Descriptive and Explanatory Roles4 The ness of Codes and the Expendability of the Code Metaphor5 The Role of Arbitrariness6 Conclusions

Intermodal image-based recognition of planar kinematic mechanisms

We present a data-driven exploratory study to investigate whether trained object detectors generalize well to test images from a different modality. We focus on the domain of planar kinematic mechanisms, which can be viewed as a set of rigid bodies connected by joints, and use textbook graphics and images of hand-drawn sketches as input modalities. The goal of our algorithm is to automatically recognize the underlying mechanical structure shown in an input image by leveraging well-known computer vision methods for object recognition with the optimizing power of multiobjective evolutionary algorithms. Taking a raw image as input, we detect pin joints using local feature descriptors in a support vector machine framework. Improving upon previous work, detection confidence depends on multiple context-based classifiers of varying image patch size and greedy foreground extraction. The likelihood of rigid body connections is approximated using normalized geodesic time, and NSGA-II is used to evolve optimal mechanisms using this data. The present work is motivated by the observation that textbook diagrams and hand-drawn sketches of mechanisms exhibit similar object structure, yet have different visual characteristics. We apply our method using various combinations of images for training and testing, and the results demonstrate a trade-off between solvability and accuracy.

Box-and-arrow explanations need not be more abstract than neuroscientific mechanism descriptions.

The nature of the relationship between box-and-arrow (BA) explanations and neuroscientific mechanism descriptions (NMDs) is a key foundational issue for cognitive science. In this article we attempt to identify the nature of the constraints imposed by BA explanations on the formulation of NMDs. On the basis of a case study about motor control, we argue that BA explanations and NMDs both identify regularities that hold in the system, and that these regularities place constraints on the formulation of NMDs from BA analyses, and vice versa. The regularities identified in the two kinds of explanation play a crucial role in reasoning about the relationship between them, and in justifying the use of neuroscientific experimental techniques for the empirical testing of BA analyses of behavior. In addition, we make claims concerning the similarities and differences between BA analyses and NMDs. First, we argue that both types of explanation describe mechanisms. Second, we propose that they differ in terms of the theoretical vocabulary used to denote the entities and properties involved in the mechanism and engaging in regular, mutual interactions. On the contrary, the notion of abstractness, defined as omission of detail, does not help to distinguish BA analyses from NMDs: there is a sense in which BA analyses are more detailed than NMDs. In relation to this, we also focus on the nature of the extra detail included in NMDs and missing from BA analyses, arguing that such detail does not always concern how the system works. Finally, we propose reasons for doubting that BA analyses, unlike NMDs, may be considered “mechanism sketches.” We have developed these views by critically analyzing recent claims in the philosophical literature regarding the foundations of cognitive science.

Mechanisms, Types, and Abstractions

Machamer, Darden, and Craver's account of the nature and role of mechanisms in the special sciences has been very influential. Unfortunately, a confusing array of ontic, epistemic, and pragmatic distinctions is required to individuate their mechanisms, mechanism schemata, and mechanism sketches. I diagnose this as a conflation of token-level causal relations with type-level relations. I propose instead that a mechanism is an abstraction that relates entity types and activity types on the model of a directed graph. Mechanisms have an ontic status distinct from the causal chains of token entities and token activities that instantiate them.

Integrating psychology and neuroscience: functional analyses as mechanism sketches

We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms, in which some struc- tural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated with multilevel mechanistic explanations.

Troubles with Mechanisms: Problems of the 'Mechanistic Turn' in Historical Sociology and Social History

AbstractThis paper discusses the prospect of the "new social history" guided by the recent work of Charles Tilly on the methodology of social and historical explanation. Tilly advocates explanation by mechanisms as the alternative to the covering law explanation. Tilly's proposals are considered to be the attempt to reshape the practices of social and historical explanation following the example set by the explanatory practices of molecular biology, neurobiology, and other recent "success stories" in the life sciences. Recent work in the philosophy of science on these practices by Peter Machamer, Lindley Darden, Carl Craver and others is used as the foil to disclose the difficulties of Tilly's project. Most important among them is the dilemma of specification: if diagrams (standard forms of the representation of mechanisms) are intended as representations of robust causal processes, they cannot be specific enough to provide complete mechanism schemata, and are bound to remain mechanism sketches. If mechanism sketches are elaborated in detail by tracing particular causal processes, they provide representations of fragile causal processes, which cannot be considered as mechanisms comparable to those in advanced life and other special sciences. Tilly's work on the explanation of mechanisms can be considered as symptomatic for the recent trend to visualize the forms of historical representation. As far as diagrams seem to be able to communicate stories in a direct way (without narrative discourse), this trend is a challenge for the theory of historical representation. The new theories of scientific explanation focusing on the explanatory practices of the life sciences can provide examples and be the source of inspiration for the work on the theory of historical and social explanation, going beyond the confines of the received framework of the covering law model of explanation.

FRAMEWORK DESIGN AND CONFIGURATION TRANSFORMATION ANALYSIS ON DEPLOYED LUNAR ROVER WHEEL

A deployed lunar rover wheel is presented to solve the contradiction between the performance requirements and the volume constraints on the lunar rover wheel. Its concept design of the deployed lunar rover is carried out based on the framework design and its configuration transformation analysis. Firstly, the primary framework is initiatively confirmed, and then the topological symmetry of the associated members on the topological diagrams is judged. By deleting repetitive associated members, the probable numbers of the practical associated members is determined. On the basis of synthesizing frameworks, all kinds of probable frameworks and their mechanism sketches are achieved. Secondly, satisfying frameworks is obtained by screening mechanism sketches, and then corresponding adjacent matrixes are made according to selected frameworks. Using the features of the adjacent matrix, configuration transformation in the deploying process and the variation range of the degrees of freedom in front-to-back transformation are analyzed on the deployed wheel mechanism. By judging the wheel motion influenced by the associated relationship of selected frameworks, evaluating the quality of all frameworks, verifying the rationality of the designed wheel framework, the framework of the lunar rover wheel is confirmed finally.

A loop configuration approach to automatic sketching of mechanisms

Sketching of mechanisms identified during the type synthesis process constitutes an important link with the subsequent dimensional synthesis process in the systematic design of mechanisms. A lack of reliable procedures for the automation of this sketching process is a hurdle towards computerization of the complete design process. In this paper, a simple and robust methodical procedure for automatically sketching every type of kinematic chain regardless of the number of links and degrees of freedom is presented. The development and application of this algorithm based on inter-loop relationship are demonstrated with the aid of several mechanism examples.

Economic Incentives for Ethical and Courteous Behavior in Medicine

Several current and proposed structural features of medical reimbursement are intended to alter the behavior of health care providers. I propose adding a structure to make physician behavior more ethical. The structure's design would be complex, but its core would be reminiscent of how a patron tips waiters. My proposal would apply the truism that society's reward systems should foster rather than undermine social goals. This idea draws on features of medicine's social background and on a theory of behavior. It challenges the taboo against the physician's financial interests being clearly present in the doctor-patient relationship and it challenges the overly pure characterization of medical ethical dilemmas that currently dominates. Detailed sketches of necessary mechanisms, such as anonymous forms for patients to complete, are offered, and connections to the insights of George Bernard Shaw are made.

An integrated system for design of mechanisms by an expert system—DOMES

Methodologies have been developed and implemented in LISP and OPS-S languages which address type synthesis of mechanisms. Graph theory and separation of structure from function concepts have been integrated into an expert system called DOMES (Design Of Mechanism by an Expert System) to effectively implement the following three activities: 1. enumeration of all non-isomorphic labelled graphs; 2. identification of those graphs which satisfy structural constraints; 3. sketching of mechanisms corresponding to a given graph.Developed theories and algorithms are applied to a robot gripper design and a variable-stroke piston engine design. The results from these two applications indicate that the automated techniques effectively identify all previously obtained solutions via manual techniques. Additional solutions are also identified and several errors of the manual process are detected. The developed methodologies and software appear to perform a complete and unbiased search of all possible candidate designs and are not prone to the errors of the manual process. Other important features of DOMES are: 1. it can learn and reason, by analogy, about a new design problem based on its experience of the problems previously solved by the system: 2. it has the capability to incrementally expand its knowledge base of rejection criteria by converting into LISP code information obtained through a query-based interactive session with a human designer; 3. it can select the set of rejection criteria relevant to a design problem from its knowledge base of rejection criteria. These procedures could become a powerful tool for design engineers, especially at the conceptual stage of design.

The Development and Application of Kinematic Icon and Inactive-Joint Concepts in Automated Mechanism Sketching

An important step within the automated type synthesis process is the generation of graphical displays of proposed mechanisms which permit designers to visualize the candidates. In this paper, the concepts of kinematic icon and inactive joint have been developed and applied to the problem of automatically generating sketches of mechanisms, given only the kinematic structure. Each different link type is treated as a separate entity: an icon, with its own predefined graphical representation. Moving-link icons, (as opposed to icons of fixed links) have special properties defined according to the joint types on the adjacent links. The locations, sizes, and orientations of the icons depend on the locations of the joints whose coordintaes may be directly assigned (in simple cases) using joint placement procedures. However, because the icons are defined by assigning a specific graphical representation to groupings of joints, and not just single joint, not all joints can be directly assigned their coordinates and this other class of kinematic joint is defined as an inactive joint. The kinematic icon and inactive joint concepts make possible the sketching of mechanisms with more complicated joint types such as prismatics and gears, for the first time in a systematic manner.