The Dark Side of Modularity: How Decomposing Problems can Increase System Complexity

Decomposition is a dominant design strategy because it enables complex problems to be broken up into loosely-coupled modules that are easier to manage and can be designed in parallel. However, contrary to widely held expectations, we show that complexity can increase substantially when natural system modules are fully decoupled from one another to support parallel design. Drawing on detailed empirical evidence from a NASA space robotics field experiment we explain how new information is introduced into the design space through three complexity addition mechanisms of the decomposition process: interface creation, functional allocation, and second order effects. These findings have important implications for how modules are selected early in the design process and how future decomposition approaches should be developed. Although it is well known that complex systems are rarely fully decomposable and that the decoupling process necessitates additional design work, the literature is predominantly focused on reordering, clustering, and/or grouping based approaches to define module boundaries within a fixed system representation. Consequently, these approaches are unable to account for the (often significant) new information that is added to the design space through the decomposition process. We contend that the observed mechanisms of complexity growth need to be better accounted for during the module selection process in order to avoid unexpected downstream costs. With this work we lay a foundation for valuing these complexity-induced impacts to performance, schedule and cost, earlier in the decomposition process.

Construction of smart sports platform in the background of healthy China

Based on related theories such as sports science, modern communication, database technology, and using literature, mathematical statistics, and other methods, the study found that: (1) the supply-demand relationship between smart sports platforms does not match; (2) the features of the completed platform are: single module, poor information service capability, insufficient information integration capability; (3) platform subsystem module selection includes 9 subsystems and 45 subsystem modules; (4) smart sports platform architecture includes: sports information and public information data, and maintenance and the four major elements of the platform’s normal software and hardware environment; (5) the government’s strong support and the company’s independent operation model are the best choices for the platform.

THE PROGRAM MODULE OF CALCULATION OF PHYSICAL WEAR OF BUILDINGS BASED ON EXPERIMENTAL ASSESSMENT OF THEIR DAMAGES

The article raises the issue of current interest of automation the process of calculation and evaluation of physical buildings deterioration. This is the most important indicator that characterizes the physical condition of the building in quantitative terms and is of interest to realtors and management companies. Calculation and assessment of physical wear and tear of buildings is a difficult and time-consuming task and is currently carried out manually or by means of Microsoft Office Excel. In this regard, the software module is proposed and developed that allows to calculate and evaluate the physical wear of buildings according to VSN 53-86 (p), which reduces the complexity and time of assessing physical wear. The software module is developed using MongoDB Server, IDE Studio 3T, Microsoft Visual Studio programming system 2017.NET Framework 4.6.1, WPF technologies, in C#, Net, using the MVVM architectural pattern. The user is provided with the following functionality when working with the software module: selection of the studied system elements, reference information of signs of their wear, automatic calculation of physical wear and reporting on the received assessment. The program module is tested at the Department of housing and communal services of the Voronezh state technical University.

A Power-Driven Stochastic-Deterministic Hierarchical High-Level Synthesis Framework for Module Selection, Scheduling and Binding

We present a power-driven hierarchical framework for module/functional-unit selection, scheduling, and binding in high level synthesis. A significant aspect of algorithm design for large and complex problems is arriving at tradeoffs between quality of solution and timing complexity. Towards this end, we integrate an improved version of the very runtime-efficient list scheduling algorithm called modified list scheduling (MLS) with a power-driven simulated annealing (SA) algorithm for module selection. Our hierarchical framework efficiently explores the problem solution space by an extensive exploration of the power-driven module-selection solution space via SA, and for each module selection solution, uses MLS to obtain a scheduling and (integrated) binding (S&B) solution in which the binding is either a regular one (minimizing number of FUs and thus FU leakage power) or power-driven with mux/demux power considerations. This framework avoids the very runtime intensive exploration of both module selection and S&B within a conventional SA algorithm, but retains the basic prowess of SA by exploring only the important aspect of power-driven module-selection in a stochastic manner. The proposed hierarchical framework provides an average of 9.5% FU leakage power improvement over state of the art (approximate) algorithms that optimize only FU leakage power, and has a smaller runtime by factors of 2.5–3x. Further, compared to a sophisticated flat simulated annealing framework and an optimal 0/1-ILP formulation for total (dynamic and leakage) FU and architecture power optimization under latency constraints, PSA-MLS provides an improvement of 5.3–5.8% with a runtime advantage of 2x, and has an average optimality gap of only 4.7–4.8% with a significant runtime advantage of a factor of more than 1900, respectively.

An Optimized Method of Module Selection During Software Development

The field of Software Engineering comes into the existence to avoid the damage caused by exploratory style of software development. Various steps specified during software development leads to the successful completion of the software under consideration. If the software is really not feasible then these steps will explain this in the initial investigation without wasting efforts in terms of time and person months. In this paper the focus is given on the Design and Implementation phases of the development process. A new method has been proposed to prepare a valid sequence of execution of modules to ease the work of implementation team. Convolution Neural Network has been used to find the best sequence of execution based on various coupling and cohesion parameters.