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In the process of robot shell design, it is necessary to match the shape of the input 3D original character mesh model and robot endoskeleton, in order to make the input model fit for robot and avoid collision. So, the purpose of this paper is to find an object of reference, which can be used for the process of shape matching.

In this work, the authors propose an interior bounded box (IBB) approach that derives from oriented bounding box (OBB). This kind of box is inside the closed mesh model. At the same time, it has maximum volume which is aligned with the object axis but is enclosed by all the mesh vertices. Based on the IBB of input mesh model and the OBB of robot endoskeleton, the authors can complete the process of shape matching. In this paper, the authors use an evolutionary algorithm, covariance matrix adaptation evolution strategy (CMA-ES), to approximate the IBB based on skeleton and symmetry of input character mesh model.

Based on the evolutionary algorithm CMA-ES, the optimal position and scale information of IBB can be found. The authors can obtain satisfactory IBB result after this optimization process. The output IBB has maximum volume and is enveloped by the input character mesh model as well.

To the best knowledge of the authors, the IBB is first proposed and used in the field of robot shell design. Taking advantage of the IBB, people can quickly obtain a shell model that fit for robot. At the same time, it can avoid collision between shell model and the robot endoskeleton.

An algorithm for Delaunay partitioning in three dimensions is given, and its use in numerical semiconductor models is examined. In particular, tetrahedral elements are found to be compatible with the Scharfetter‐Gummel discretization of the stationary continuity equations associated with such models, using the Voronoi cross‐sections for each edge in the obtained network. For tetrahedral elements, however, the Voronoi cross‐sections do not coincide with those previously shown to be compatible with the Scharfetter‐Gummel method.

This paper deals with the organizing of interactive product development. Developing products in interaction between firms may provide benefits in terms of specialization, increased innovation, and possibilities to perform development activities in parallel. However, the differentiation of product development among a number of firms also implies that various dependencies need to be dealt with across firm boundaries. How dependencies may be dealt with across firms is related to how product development is organized. The purpose of the paper is to explore dependencies and how interactive product development may be organized with regard to these dependencies.

The analytical framework is based on the industrial network approach, and deals with the development of products in terms of adaptation and combination of heterogeneous resources. There are dependencies between resources, that is, they are embedded, implying that no resource can be developed in isolation. The characteristics of and dependencies related to four main categories of resources (products, production facilities, business units and business relationships) provide a basis for analyzing the organizing of interactive product development.

Three in-depth case studies are used to explore the organizing of interactive product development with regard to dependencies. The first two cases are based on the development of the electrical system and the seats for Volvo’s large car platform (P2), performed in interaction with Delphi and Lear respectively. The third case is based on the interaction between Scania and Dayco/DFC Tech for the development of various pipes and hoses for a new truck model.

The analysis is focused on what different dependencies the firms considered and dealt with, and how product development was organized with regard to these dependencies. It is concluded that there is a complex and dynamic pattern of dependencies that reaches far beyond the developed product as well as beyond individual business units. To deal with these dependencies, development may be organized in teams where several business units are represented. This enables interaction between different business units’ resource collections, which is important for resource adaptation as well as for innovation. The delimiting and relating functions of the team boundary are elaborated upon and it is argued that also teams may be regarded as actors. It is also concluded that a modular product structure may entail a modular organization with regard to the teams, though, interaction between business units and teams is needed. A strong connection between the technical structure and the organizational structure is identified and it is concluded that policies regarding the technical structure (e.g. concerning “carry-over”) cannot be separated from the management of the organizational structure (e.g. the supplier structure). The organizing of product development is in itself a complex and dynamic task that needs to be subject to interaction between business units.

Sound real estate decisions are both financially and strategically essential to corporate success. Given their importance, this paper aims to illustrate how the actor network theory (ANT) can be a valuable alternate lens to bounded rational and political perspectives in providing insights into corporate real estate decision‐making processes.

This exploratory investigation uses a case study approach to retroactively examine the real estate decision‐making process over five to seven years in four organizations ranging in size from four to 125 employees. The study uses multiple data sources including 25 in‐depth interviews, site visits, archival data, websites, documents, and email correspondence.

Using the constructs of ANT, the findings reveal how real estate decision making involves iterative but identifiable phases through which heterogeneous actors seek to converge diverse interests and where artifacts affect behaviors and outcomes as much as or sometimes more than their human creators.

Given the case study method, this research lacks generalizability. Researchers are encouraged to test the findings in different contexts.

The ANT perspective helps managers faced with real estate decisions to appreciate the relevant matrix of need, power, and interests; recognize and seek to control the power of artifacts; and, view real estate decision making not as simply making a choice among logical alternatives, but as orchestrating a long, complex process.

This investigation compares the perspectives of ANT to the classical bounded rational and political lenses in examining corporate real estate decision making; demonstrates ANT's value in providing additional insights; and, discusses its implications for understanding and managing these complex processes.

The purpose of this paper is to present an algorithm for performing collision avoidance with robotic manipulators.

The method does not require any a priori knowledge of the motion of other objects in its environment. Moreover, it is computationally efficient enough to be implemented in real time. This is achieved by constructing limitations on the motion of a manipulator in terms of its allowable instantaneous velocity. Potential collisions and joint limits are formulated as linear inequality constraints. Selection of the optimal velocity is formulated as a convex optimization and is solved using an interior point method.

Experimental results with two industrial arms verify the effectiveness of the method and illustrate its ability to easily handle many simultaneous potential collisions.

The resulting algorithm allows arbitrary motions commanded to the robot to be modified on‐line in order to guarantee optimal real‐time collision avoidance behaviors.

The main objective is to develop an efficient BEM scheme for the numerical solution of two‐dimensional heat problems. Our scheme will be of the re‐initialization type, in which the domain integrals are computed by a recursion relation which depends only on the boundary temperature and flux at previous time step. To obtain the re‐initialization approach, we will use in the integral representation formula a Green function corresponding to zero temperature in a box containing the original domain, instead of using the classical free space fundamental solution. This Green function is given in terms of the original fundamental solution plus a regular solution of the heat equation inside the domain under consideration. It can therefore be used in the integral representation formula of the heat equation (direct formulation) to obtain the solution of a heat problem in such a domain. The Green function mentioned can be obtained by the images method, and the resulting source series can also be rewritten in terms of a double Fourier series, that we will use in the domain integral of the integral representation formula to transform such integral into equivalent surface integrals.

A new expression of the philosophy framework of pansystems theory is stated. Some generalized quantification formulae are developed. Concrete contents include: new forms of panderivative and pansymmetry, meta‐equation, eight‐counter methodology, pansystems relativity, dialectical logic, panbox principle, complex systems, systems engineering, analytic hierarchy process, general living systems, economy‐sociology, clustering, topology, morphology, master equation, approximation‐transforming theory, equivalence theory of dynamics of electromagnetic media, etc.

The refined process structure tree (RPST), the hierarchy of non-overlapping single-entry single-exit (SESE) regions of a process model, has been utilized for better comprehension and more efficient analysis of business process models. Existing RPST methods, based on the triconnected components of edges, fail to identify a certain type of SESE region. The purpose of this paper is to introduce an alternative method for generating a complete RPST utilizing rather simple techniques.

The proposed method first focuses on the SESE regions of bonds and rigids, from the innermost ones to the outermost ones, utilizing dominance and post-dominance relations. Then, any SESE region of a series nested in a bond or a rigid is identified with a depth-first search variation. Two-phase algorithms and their completeness proofs, a software tool incorporating visualization of stepwise outcomes, and the experimental results of the proposed method are provided.

The proposed method utilizes simple techniques that allow their straightforward implementation. Visualization of stepwise outcomes helps process analysts to understand the proposed method and the SESE regions. Experiments with 604 SAP reference models demonstrated the limitation of the existing RPST methods. The proposed method, however, completely identified all types of SESE regions, defined with nodes, in less computation time than with the old methods.

Each triconnected component of the undirected version of a process model is associated with a pair of boundary nodes without discriminating between the entry and the exit. Here, each non-atomic SESE region is associated with two distinct entry and exit nodes from the original model in the form of a directed graph. By specifying the properties of SESE regions in more comprehensible ways, this paper facilitates a deeper understanding of SESE regions rather than relying on the resulting RPST.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.