Qianli Xu

Integrated Vehicle Configuration System-Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.

User Experience Modeling and Simulation for Product Ecosystem Design Based on Fuzzy Reasoning Petri Nets

Product ecosystem design entails complex user experience (UX) that involves interactions among multiple users, products, and the ambience. This paper aims to capture causal relationships between UX and design elements and in turn to provide decision support to product ecosystem analysis. A fuzzy reasoning Petri net is developed to deal with the uncertainty, complexity, and dynamics associated with UX modeling. Reasoning of diverse constructs of UX is embedded in the fuzzy production rules that are derived from self-report UX data based on rough set mining. A fuzzy reasoning algorithm is implemented to perform parallel inference by multicriteria rules and to simulate most likely UX under different ambient factors. A case study of subway station UX design demonstrates the potential of product ecosystem FRPN formulation.

A wearable virtual guide for context-aware cognitive indoor navigation

In this paper, we explore a new way to provide context-aware assistance for indoor navigation using a wearable vision system. We investigate how to represent the cognitive knowledge of wayfinding based on first-person-view videos in real-time and how to provide context-aware navigation instructions in a human-like manner. Inspired by the human cognitive process of wayfinding, we propose a novel cognitive model that represents visual concepts as a hierarchical structure. It facilitates efficient and robust localization based on cognitive visual concepts. Next, we design a prototype system that provides intelligent context-aware assistance based on the cognitive indoor navigation knowledge model. We conducted field tests and evaluated the systems efficacy by benchmarking it against traditional 2D maps and human guidance. The results show that context-awareness built on cognitive visual perception enables the system to emulate the efficacy of a human guide, leading to positive user experience.

Attention-based addressee selection for service and social robots to interact with multiple persons

For robots to perform interaction with multiple persons, they have to be able to identify the addressees to interact with. We classify the methods of addressee detection and selection into two categories, namely, passive and active approaches. For passive approaches, the robot is programmed to detect a predefined signal, e.g., a voice command or a specific gesture, from a person who is supposed to be the addressee. In contrast, for active approaches, the robot is able to select a person as an addressee based on subtle cues that are inferred from the human pose, gaze, and facial expression. We present two new approaches for attention-based addressee selection, one is a passive method and the other is an active method. The passive method is designed for the robot to recognize common hand-waving gesture, where a Bayessian ensemble approach is proposed to fuse hand detections from depth segmentation, palm shape, skin color, and body pose. The active method is developed for the robot to perform natural interaction with multiple persons. It employs a novel human attention estimation algorithm based on human detection, tracking, upper body pose recogni-tion, face detection, gaze detection, lip motion analysis, and facial expression recognition. Extensive experiments have been conducted and the effectiveness of the proposed approaches is reported.

Elicitation and analysis of affective needs in vehicle design

This article describes a methodology for elicitation and analysis of affective needs for vehicle design. Driven by the concept of citarasa or emotional intent, the method has five steps. First, a model of emotional intent was conceptualised; second, a semantic framework of citarasa words was developed that mapped words to specific vehicle components to form a citarasa ontology; third, customer citarasa were elicited in the field using probe interview technique; fourth, affective needs were refined through Web survey; and fifth, the elicited citarasa were analysed using data mining techniques and the citarasa analysis tool. The tool is linked to the citarasa database that enables analysis of affective needs in several countries in Europe and Asia. The system has been technically verified, validated and tested for usability with consumers and automotive end-users.

Domain-based production configuration with constraint satisfaction

Production configuration is well recognised as an effective means of planning production processes for product families. The major challenge of production configuration originates from the handling of the numerous constraints associated with product and process variety. This paper develops a constraint satisfaction approach to facilitate production configuration decisions regarding constraint identification, representation, and evaluation. A domain-based model is formulated to conceptualise the production configuration process, involving inter-connections among multiple domains in conjunction with diverse domain decision variables and constraints. Within the domain framework, production configuration is formulated as a constraint satisfaction problem (CSP), which is solved using constraint heuristic search. Within constraint heuristic search, a decision propagation structure incorporating a connectionist approach is developed to facilitate the exploration of solution spaces. A case study of textile spindle production configuration is elaborated to illustrate the feasibility and potential of the domain-based CSP model for production configuration.

A knowledge-based system for process family planning

Purpose - The purpose of this paper is twofold. First, it is to introduce a knowledge-based system for planning processes for families of final products, instead of component items, be they parts or assemblies. Second, it is to demonstrate the feasibility and potential of a prototypical system developed for planning processes families for truck families from a multinational company. Design/methodology/approach - The authors first identify the challenges in planning process families, including data and knowledge representation and constraint handling. To accommodate these challenges, the paper adopts the integrated product and process structure (IP2S) and colored timed Petri nets (CTPNs) in the proposed knowledge-based process family planning system. On top of the IP2S and CTPNs, XML-based knowledge representation is employed to alleviate the difficulties in modelling complex product and process family data and planning knowledge while enabling information exchange across different operating platforms. In addition, in accordance with the correspondence between PNs and knowledge-based systems, a mechanism is designed to cope with the generation of production rules, which model constraints. Findings - The proposed system is able to automatically generate production processes for customized products. At a higher level, such production processes provide input (e.g. operations, machines) to downstream activities for planning process details to manufacture component parts or component assemblies. Research limitations/implications - Traditional trial and error approaches to planning processes limit production performance improvement when companies need to timely produce diverse customized products. Knowledge-based systems should be developed to help companies better plan production processes based on the available manufacturing resources. Originality/value - Unlike most reported studies addressing either detailed process planning or assembly planning for component parts or component assemblies, this study tackles process planning for final products, in attempting to maintain production efficiency from a holistic view.

Enhancing Social Interaction with Seamless Face Recognition on Google Glass: Leveraging opportunistic multi-tasking on smart phones

Wearable devices offer immense opportunities in both consumer and enterprise domains due to the hands-free interaction modality and the ability to provide information in real-time. However, due to hardware limitations, it presents a notable challenge to complex applications that have stringent demands on computational efficiency. Leveraging on the computing power of a connected mobile device, we propose a new multi-threaded asynchronous structure to implement opportunistic multi-tasking. We demonstrate an application of the structure for seamless face recognition for social interactions. The experimental studies show that the proposed structure can achieve better performance than a sequential synchronous one. The proposed method can be extended to similar applications in wearable devices.

A methodology integrating Petri nets and knowledge-based systems to support process family planning

Planning production processes for product families have been well recognised as an effective means of achieving successful product family development. However, most existing approaches do not lend themselves to planning production processes with focus on the optimality of the cohort of a product family. This paper addresses process family planning for product families. In view of the advantages of Petri nets (PNs) for modelling large systems, the potential of knowledge-based systems (KBSs) for solving complex problems and the analogy in between, we develop a methodology by integrating PNs and KBSs to support process family planning. An integrated product–process family structure, called IP2S, is proposed to organise all data pertaining to a product family and the corresponding process family, thereby anchoring planning to one platform. With the IP2S, a formal PN model of process family planning is further developed by integrating the principles of several well-defined PN extensions. Thus, this paper also contributes to visualising the dynamic behaviours and reasoning behind process family planning. The methodology is applied to process family planning for a truck family. The preliminary results demonstrate the feasibility and potential of using the methodology to support process family planning.

A Wearable Virtual Usher for Vision-Based Cognitive Indoor Navigation

Inspired by progresses in cognitive science, artificial intelligence, computer vision, and mobile computing technologies, we propose and implement a wearable virtual usher for cognitive indoor navigation based on egocentric visual perception. A novel computational framework of cognitive wayfinding in an indoor environment is proposed, which contains a context model, a route model, and a process model. A hierarchical structure is proposed to represent the cognitive context knowledge of indoor scenes. Given a start position and a destination, a Bayesian network model is proposed to represent the navigation route derived from the context model. A novel dynamic Bayesian network (DBN) model is proposed to accommodate the dynamic process of navigation based on real-time first-person-view visual input, which involves multiple asynchronous temporal dependencies. To adapt to large variations in travel time through trip segments, we propose an online adaptation algorithm for the DBN model, leading to a self-adaptive DBN. A prototype system is built and tested for technical performance and user experience. The quantitative evaluation shows that our method achieves over 13% improvement in accuracy as compared to baseline approaches based on hidden Markov model. In the user study, our system guides the participants to their destinations, emulating a human usher in multiple aspects.