Adaptive Support System Research Articles

Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support

Precision irrigation based on the “speaking plant” approach can save water and maximize crop yield, but implementing irrigation control can be challenging in system integration and decision making. In this paper we describe the design of an adaptable decision support system and its integration with a wireless sensor/actuator network (WSAN) to implement autonomous closed-loop zone-specific irrigation. Using an ontology for defining the application logic emphasizes system flexibility and adaptability and supports the application of automatic inferential and validation mechanisms. Furthermore, a machine learning process has been applied for inducing new rules by analyzing logged datasets for extracting new knowledge and extending the system ontology in order to cope, for example, with a sensor type failure or to improve the accuracy of a plant state diagnosis. A deployment of the system is presented for zone specific irrigation control in a greenhouse setting. Evaluation of the developed system was performed in terms of derivation of new rules by the machine learning process, WSN performance and mote lifetime. The effectiveness of the developed system was validated by comparing its agronomic performance to traditional agricultural practices.

Adaptive Body Support System for Aircraft Seat

The development of new technology enables the comfort service becoming an important requirement in the air travel industry. Long haul air travel may cause different physiological and psychological discomfort of aircraft passenger. Most of the aircraft company treats the passenger comfort as a main priority for the user’s acceptance of their airline. In order to improve the aircraft passenger body comfort during long haul air travel, an adaptive body support system was developed and embedded in the economy class aircraft seat. In this paper, we describe the development of adaptive body support system.

An Adaptive Driver Support System

The aim of this study was to test the implementation of an adaptive driver support system. Providing support might not always be desirable from a safety perspective, as support may lead to problems related to a human operator being out of the loop. In contrast, adaptive support systems are designed to keep the operator in the loop as much as possible by providing support only when necessary. A total of 31 experienced drivers were exposed to three modes of lane-keeping support nonadaptive, adaptive, and no support. Support involved continuously updated lateral position feedback shown on a head-up display. When adaptive, support was triggered by performance-based indications of effort investment. Narrowing lane width and increasing density of oncoming traffic served to increase steering demand, and speed was fixed in all conditions to prevent any compensatory speed reactions. Participants preferred the adaptive support mode mainly as a warning signal and tended to ignore nonadaptive feedback. Furthermore, driving behavior was improved by adaptive support in that participants drove more centrally, displayed less lateral variation and drove less outside the lane's delineation when support was in the adaptive mode compared with both the no-support mode and the nonadaptive support mode. A human operator is likely to use machine-triggered adaptations as an indication that thresholds have been passed, regardless of the support that is initiated. Therefore supporting only the sensory processing stage of the human information processing system with adaptive automation may not feasible. These conclusions are relevant for designing adaptive driver support systems.

Adaptation patterns as a conceptual tool for designing the adaptive operation of CSCL systems

While adaptive collaboration support has become the focus of increasingly intense research efforts in the CSCL domain, scarce, however, remain the research-based evidence on pedagogically useful ideas on what and how to adapt during the collaborative learning activity. Based principally on two studies, this work presents a compilation of adaptation patterns, which offer the basis for designing the adaptive operation of a CSCL system. In the first study 12 postgraduate students were engaged in a pyramid-type scripted collaborative activity, while in the second study 36 undergraduate students worked in dyads, being adaptively supported by domain-specific prompts in the LAMS environment. The analysis of the learning experiences provided the basis for identifying a number of possible adaptation patterns. An adaptation pattern is described as a pedagogically useful and well-targeted adjustment process that can be initiated by the teacher or the CSCL system, in order to foster an improved learning setting when specific conditions occur during the collaborative learning activity. This pattern-based approach is presented within the notion of adaptive collaboration support. Adaptive collaboration support systems model various entities and processes of the collaborative activity to adaptively support students during run time, in order to provide enhanced learning experiences. Additionally, a prototype methodology (DeACS) is suggested for identifying and evaluating useful adaptation patterns for possible implementation in CSCL systems.

An adaptive navigation support system for conducting context-aware ubiquitous learning in museums

In context-aware ubiquitous learning, students are guided to learn in the real world with personalized supports from the learning system. As the learning resources are realistic objects in the real world, certain physical constraints, such as the limitation of stream of people who visit the same learning object, the time for moving from one object to another, and the environmental parameters, need to be taken into account. Moreover, the values of these context-dependent parameters are likely to change swiftly during the learning process, which makes it a challenging and important issue to find a navigation support mechanism for suggesting learning paths for individual students in real time. In this paper, the navigation support problem for context-aware ubiquitous learning is formulated and two navigation support algorithms are proposed by taking learning efficacy and navigation efficiency into consideration. From the simulation results of learning in a butterfly museum setting, it is concluded that the innovative approach is helpful to the students to more effectively and efficiently utilize the learning resources and achieve better learning efficacy.

CTRL: A research framework for providing adaptive collaborative learning support

There is evidence suggesting that providing adaptive assistance to collaborative interactions might be a good way of improving the effectiveness of collaborative activities. In this paper, we introduce the Collaborative Tutoring Research Lab (CTRL), a research-oriented framework for adaptive collaborative learning support that enables researchers to combine different types of adaptive support, particularly by using domain-specific models as input to domain-general components in order to create more complex tutoring functionality. Additionally, the framework allows researchers to implement comparison conditions by making it easier to vary single factors of the adaptive intervention. We evaluated CTRL by designing adaptive and fixed support for a peer tutoring setting, and instantiating the framework using those two collaborative scenarios and an individual tutoring scenario. As part of the implementation, we integrated pre-existing components from the Cognitive Tutor Algebra (CTA) with custom-built components. The three conditions were then compared in a controlled classroom study, and the results helped us to contribute to learning sciences research in peer tutoring. CTRL can be generalized to other collaborative scenarios, but the ease of implementation relates to the complexity of the existing components used. CTRL as a framework has yielded a full implementation of an adaptive support system and a controlled evaluation in the classroom.

An Adaptive Consensus Support Model for Group Decision-Making Problems in a Multigranular Fuzzy Linguistic Context

Different consensus models for group decision-making (GDM) problems have been proposed in the literature. However, all of them consider the consensus reaching process a rigid or inflexible one because its behavior remains fixed in all rounds of the consensus process. The aim of this paper is to improve the consensus reaching process in GDM problems defined in multigranular linguistic contexts, i.e., by using linguistic term sets with different cardinality to represent experts' preferences. To do that, we propose an adaptive consensus support system model for this type of decision-making problem, i.e., a process that adapts its behavior to the agreement achieved in each round. This adaptive model increases the convergence toward the consensus and, therefore, reduces the number of rounds to reach it.

The effect of adaptive performance support system on learning achievements of students

The study compares the effectiveness of two performance support systems, adaptive and non-adaptive, on learning achievements of engineering students. In addition, the research design controls for a possible effect of learning style. The analysis reveals that students working with an adaptive performance support system score significantly higher than students using a non-adaptive performance system on a performance test across different learning styles. The only variation in the two experimental conditions, manipulated in the study, is embedded adaptive arrangement based on learning style. Embedded adaptation mode accommodates learning preferences of students through the structure of learning content as an association between types of learning content and different learning styles is assumed. Learning style does not produce a significant difference in the performance achievements of students and there is no indication for an interaction effect between performance support system as a method of instruction and learning style. These results are explained by two theoretical positions introduced in the background of the study, namely coping behaviour and the distinction between level and style type of cognitive constructs.

The Process Engineering Adaptive Modeling Support System Based on Visualization Cluster Analysis

The objective of this paper is to implement an ”adaptive modeling support system (AMSS)”, which includes modeling, model understanding and modeling adaptive improvement. The paper identifies the knowledge in AMSS which is based on visualization cluster analysis (VCA) to support the iterative cycle, and focuses on studying the learning methods to obtain such knowledge and architecture to support the capabilities. OSP (the Oil Stabilization Process) is used to demonstrate the functionality of AMSS that results in an effective process modeling tool with high solution quality and consistency.