Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

Model Based Biomass System Design of Feedstock Supply Systems for Bioenergy Production

Engineering feedstock supply systems that deliver affordable, high-quality biomass remains a challenge for the emerging bioenergy industry. Cellulosic biomass is geographically distributed and has diverse physical and chemical properties. Because of this feedstock supply systems that deliver cellulosic biomass resources to biorefineries require integration of a broad set of engineered unit operations. These unit operations include harvest and collection, storage, preprocessing, and transportation processes. Design decisions for each feedstock supply system unit operation impact the engineering design and performance of the other system elements. These interdependencies are further complicated by spatial and temporal variances such as climate conditions and biomass characteristics. This paper develops an integrated model that couples a SQL-based data management engine and systems dynamics models to design and evaluate biomass feedstock supply systems. The integrated model, called the Biomass Logistics Model (BLM), includes a suite of databases that provide 1) engineering performance data for hundreds of equipment systems, 2) spatially explicit labor cost datasets, and 3) local tax and regulation data. The BLM analytic engine is built in the systems dynamics software package Powersim™. The BLM is designed to work with thermochemical and biochemical based biofuel conversion platforms and accommodates a range of cellulosic biomass types (i.e., herbaceous residues, short-rotation woody and herbaceous energy crops, woody residues, algae, etc.). The BLM simulates the flow of biomass through the entire supply chain, tracking changes in feedstock characteristics (i.e., moisture content, dry matter, ash content, and dry bulk density) as influenced by the various operations in the supply chain. By accounting for all of the equipment that comes into contact with biomass from the point of harvest to the throat of the conversion facility and the change in characteristics, the BLM evaluates economic performance of the engineered system, as well as determining energy consumption and green house gas performance of the design. This paper presents a BLM case study delivering corn stover to produce cellulosic ethanol. The case study utilizes the BLM to model the performance of several feedstock supply system designs. The case study also explores the impact of temporal variations in climate conditions to test the sensitivity of the engineering designs. Results from the case study show that under certain conditions corn stover can be delivered to the cellulosic ethanol biorefinery for $35/dry ton.

Identifying the Material Information Requirements for Sustainable Decision Making

Materials play a central role in product manufacturing, contributing to each phase of product development in the form of either a component or process material. As the product revolves around materials, so does much of the product information. Material information plays a significant role in the decision making process at any stage of the product life cycle, especially with respect to the sustainability of a product. Material information in the manufacturing stages of a product’s life cycle will relate to the processes used in manufacturing and assembling individual components. The material properties may determine what processes can be used and how these processes should be controlled. To support sustainable manufacturing, the impacts of material choice should be considered during design, when resources are being committed. When comparing material alternatives at design time, it is not as simple as saying one material is “more sustainable” than another. Many different factors determine the sustainability of a product, and each of these factors may be influenced by multiple material properties represented through various information requirements. In order to develop a material information model that can satisfy these information requirements, we need to carefully study the requirements from an information modeling perspective. In this paper, we use activity models to describe design and manufacturing scenarios that rely on the availability of proper material information for sustainability decision making. We will use these models to first define specific scenarios and then to identify the types of material information that is typically required in these scenarios, and collect and categorize key concepts. Based on this study, we will make recommendations that will aid the development of a useful material information model for sustainable decision making.

Automatic Verification of Modeling Rules in Systems Engineering for Mechatronic Systems

Mechatronics is the close interaction of mechanics, electronics, control engineering and software engineering. The increasing complexity of mechatronic systems results in a challenging development process and particularly requires a consistent comprehension of the tasks between all the engineers involved. Especially during the early design phases, the communication and cooperation between the mechanical, electrical, control and software engineers is necessary to establish a basis for efficient and effective product development. The approach of Model-Based Systems Engineering focuses on this aspect by means of an abstract but superordinate system model. It enables a holistic view of the system. The system model can be specified using the Systems Modeling Language (SysML). The language allows many degrees of freedom to specify a fact, bearing in mind that different system architects can specify the same fact in different ways. This leads to system models that can be interpreted in many ways. Thus, these models are hard to consistently compare and interpret, resulting in communication issues. In order to tackle this problem, we present a concept that uses modeling rules supporting model comparability. We formalize them by means of checks implemented in the programming language Java and the Object Constraint Language (OCL) in order to automatically verify the system model’s compliance with these rules.

Knowledge Base Representation for Axiomatic Design Through Ontologies

Axiomatic Design has been applied and developed as a tool, offering a scientific basis for design and improving design activities. Axiomatic Design has been used in various fields such as software system design, structure design, and product design. However, several challenges and limitations exist in Axiomatic Design including: the inconsistency in identifying design parameters, existence of coupled design, and multiple groups of functional requirements and design parameters. Aimed at using Axiomatic Design to generate conceptual solutions in engineering design while overcoming its limitations, a formal ontology is developed. The ontology defines functional requirements, design parameters, concepts, components and variables and their relationships. Axioms and rules of Axiomatic Design for the ontology are summarized. The Axiomatic Design ontology is applied to the design of a car seat as an example generating several concepts, and then compared and analyzed multiple groups of the concepts with the help of Axiomatic Design rules. More design ideas can be generated by combining detailed concepts as the higher level possible solutions.

An Analysis of Engineers Information Seeking Activity

Information seeking is an important part of the engineering design process. In this context the Internet has become a significant source of information, shaping the way engineers work and interact. Current work has focused on characterizing this activity in terms of total time allocated to different information sources or overall tasks, such as evaluating. However, these approaches do not give an understanding of how engineers information seeking affects their problem solving activity and ultimately their performance in the context of the design process. As such, a new approach is needed to decompose the complexity of information seeking activity in order to more effectively support the evolving needs of engineering designers and design researchers. This paper addresses these issues by using an experimental study and network visualization technique to analyze Internet based information seeking activity and its affect on engineers overall performance during the information seeking/feasibility stage of the design process. The study uses both final year trainee engineers and practicing engineers in order to more fully explore the different modes of information seeking activity. With the study complete, the visual network analysis is used to explore patterns of information seeking and other design activity. Based on the results, three clearly differentiated types of information seeking activity are identified and discussed.

Low Cost Mixed Reality System Using Projectors for Evaluation of Product Design (Shape and Function Structure)

Three-dimensional models (CAD models) are constructed in the design processes of products because they are effective for design evaluation processes using CAE systems and manufacturing processes using CAM systems. However, mock-ups or prototypes are still required in the evaluation processes of designability and operability of products because the evaluation of the operations of real products is essential. It is however time-consuming and costly to make prototypes or to develop trial products for evaluation. For this problem, considerable studies have been conducted on the use of mixed reality technology by overlaying an image of the design model onto a physical model using a Head-Mounted Display (HMD) to evaluate the designability and operability of a product. Such technology reduces the need for making physical mock-ups (prototypes and trial products), but HMDs have drawbacks such as causing motion sickness and physical weight, bulkiness of the display, and high costs. In this paper, a method using projectors is proposed to establish mixed reality technology which does not have the drawbacks of HMDs. A mixed reality system was constructed according to the proposed method, and applied for evaluating designability and operability of products without physical mock-ups. In the mixed reality space built by the system, the functions of a product can be held in the hand as if they were real products.

Integrating Biological and Engineering Ontologies

As methods for engineering data acquisition improve, methods for storing, generating knowledge from, and sharing that data for efficient reuse have become more important. Knowledge management in the engineering community can greatly benefit from advancements made in knowledge management in biology. The biological community has already made progress in knowledge management through projects such as the Gene Ontology and CellML, and it behooves the engineering community to learn from their successes. Engineering and biology overlap in the field of biosimulation, (i.e. finite-element analysis of biological systems, see www.biomesh.org) which gives an opportunity to integrate successful ontologies from the biology community into the engineering community. Previous research has led to the creation of the Biomesh project, which is a collection of biological finite element (FE) models. These FE models relate to a particular anatomical structure of an organism, and to the set of biological material properties associated with the models. Thus, knowledge management for this application requires knowledge integration from three distinct fields: engineering (materials and models), anatomy, and biological classification. The existing e-Design Framework offers the Engineering Analysis Models ontology and Materials ontology to store knowledge about materials and FE models. Similarly, the existing Minimal Anatomical Terms ontology and the NCBI Organismal Classification taxonomy were used to store information about anatomy and biological classification, respectively. In this paper these ontologies are interlinked in a single, synergistic ontology to expose and integrate knowledge in a transparent manner between previously disparate domains. A case study is presented to demonstrate the usefulness of the approach in which knowledge from a biological material and FE model are methodically stored in the new ontology, and the organismal classification and anatomical structure of the model are immediately exposed to the user.

Complex System Design Verification Using Assumption Generation

In the era of large complex systems with continuous and discrete event components, it is critical to establish a complete design verification strategy to determine whether a system satisfies certain safety properties. However, traditional approaches for the verification of such a complex system lack the ability to take into account all possible system states, efficiently model all component interactions, and accurately quantify the risks and uncertainties. This paper presents a methodology for system-level design of complex systems verification based on compositional model checking. This methodology relies on assumption generation and on the domain independent compositional rules for correctness proof of the design of physical systems. The objective is to present a case study for applying the existing automated compositional verification techniques and observing the characteristics of the verification model. The main advantage of this method is that it enables the designer to verify the safety properties of the system without requiring the detail knowledge of the internal actions of the system. The under-approximate context model of the system design is constructed and, in an iterative approach, its safety properties are analyzed until a violation of a property is found and an execution trace called a counter example is produced. In the case of safety requirements violation, the early generation of counter examples leads to faster design verification.

Outlining Nine Major Design Challenges of Open, Decentralized, Adaptive Cyber-Physical Systems

Open, decentralized, adaptive cyber-physical systems (ODA-CPSs) have countless novel structural attributes and functional affordances. Consequently, they pose many design and engineering challenges. This paper identifies and analyzes nine of them. They are: (i) handling aggregative complexity, (ii) establishing static and dynamic compositional synergy, (iii) managing dynamic and evolutionary operation in time, (iv) multi-abstraction-based modeling, (v) system integrity verification and behavior validation, (vi) achieving dynamic scalability towards meta-systems, (vii) transformation of big data, (viii) employing testable surrogate prototyping, and (ix) attaining robust social compliance. These challenges should be addressed already in the course of conceptualization and design of these systems. It is shown that a kind of duality is hiding practically in each of these challenges, which are caused by the concurrence of short term dynamic behavior and long term evolution of ODA-CPSs. Though interrelated, these two aspects still need to be handled separate. In order to response effectively to the above challenges, foundational research and operative research need to produce new transdisciplinary insights and new practical principles, respectively. First, previous efforts in these dimensions are critically evaluated. Then it is circumscribed what new knowledge is needed in order to cope with the considered major challenges. Putting everything together, the paper concludes that the grand challenge is in the lack of a dedicated transdisciplinary design theory that could explain how ODA-CPSs should be ideated and synthesized, and that would allow the development of a comprehensive design methodology and computational support tools. Future research will attempt to propose concrete solutions for the discussed challenges and most probably identify other emerging ones.