• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.29-38
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• 2009

In the computing environment with heterogeneous resources, a job scheduling model is necessary for effective resource utilization and high-speed data processing. And, the job scheduling model has to cope with a dynamic change in the condition of resources. There have been lots of researches on resource estimation methods and heuristic algorithms about how to distribute and allocate jobs to heterogeneous resources. But, existing researches have a weakness for system compatibility and scalability because they do not support the standard language. Also, they are impossible to process jobs effectively and deal with a variety of computing situations in which the condition of resources is dynamically changed in real-time. In order to solve the problems of existing researches, this paper proposes a semantic computing-based dynamic job scheduling model that defines various knowledge-based rules for job scheduling methods adaptable to changes in resource condition and allocate a job to the best suited resource through inference. This paper also constructs a resource ontology to manage information about heterogeneous resources without difficulty as using the OWL, the standard ontology language established by W3C. Experimental results shows that the proposed scheduling model outperforms existing scheduling models, in terms of throughput, job loss, and turn around time.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.193-202
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• 2017

Development of Intelligent Systems involves effective integration of large-scaled knowledge processing and understanding, human-machine interaction, and intelligent services. Especially, in our project for development of a self-growing knowledge-based system with inference methodologies utilizing the big data technology, we are building a platform called WiseKB as the central knowledge base for storing massive amount of knowledge and enabling question-answering by inferences. WiseKB thus requires an effective methodology to analyze diverse requirements convoluted with the integration of various components of knowledge representation, resource management, knowledge storing, complex hybrid inference, and knowledge learning, In this paper, we propose an integrated requirement analysis method that blends the traditional sequential method and the iterative-incremental method to achieve an efficient requirement analysis for large-scale systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.850-858
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• 2022

To deploy Gate Recurrent Units (GRU) on resource-constrained embedded devices, this paper presents a reconfigurable FPGA-based GRU accelerator that enables structured compression. Firstly, a dense GRU model is significantly reduced in size by hybrid quantization and structured top-k pruning. Secondly, the energy consumption on external memory access is greatly reduced by the proposed reuse computing pattern. Finally, the accelerator can handle a structured sparse model that benefits from the algorithm-hardware co-design workflows. Moreover, inference tasks can be flexibly performed using all functional dimensions, sequence length, and number of layers. Implemented on the Intel DE1-SoC FPGA, the proposed accelerator achieves 45.01 GOPs in a structured sparse GRU network without batching. Compared to the implementation of CPU and GPU, low-cost FPGA accelerator achieves 57 and 30x improvements in latency, 300 and 23.44x improvements in energy efficiency, respectively. Thus, the proposed accelerator is utilized as an early study of real-time embedded applications, demonstrating the potential for further development in the future.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Asia Marketing Journal
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• v.14 no.2
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• pp.23-37
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• 2012

Individuals often make their judgments on the basis of the ease or difficulty with which information comes to mind (for reviews, see Greifeneder, Bless, and Pham 2010; Schwarz 1998, 2004). Recent research, however, has documented that variables known to determine the degree of cognitive resources invested in information processing such as personal relevance (Grayson and Schwarz 1999; Rothman and Schwarz 1998), accuracy motivation (Aarts and Dijksterhuis 1999), and processing capacity (Menon and Raghubir 2003) can affect the extent to which individuals draw on metacognitive difficulty in making their judgments. The primary aim of this research is thus to investigate whether individuals with substantial cognitive resources or those with lack of cognitive resources are more likely to draw on metacognitive difficulty when making their product evaluations. The findings from two laboratory experiments indicate that individuals who perceive a greater level of fit between their self-regulatory orientation and temporal construal (Experiment 1), and between their self-construal and the type of product benefit appeal (Experiment 2) are more likely than those who perceive the lack of such fit to evaluate a target product less positively after thinking of many rather than a few positive reasons. The findings provide supporting evidence for the two-stage backward inference process involved with the effect of metacognitive difficulty on consumer judgments in that consumer judgments based on metacognitive difficulty may require greater cognitive resources than those based on the content of information generated. Also, the current research documents further empirical evidence for the relationship between self-regulatory orientation-construal level fit and cognitive resources such that perceived regulatory-construal level fit can increase consumer willingness to invest cognitive resources into their judgment tasks. Last, the findings can help marketers differentiate purchase situations where asking consumers to think of many positive benefits from purchase situations where asking consumers to think of a few key benefits is relatively more beneficial.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.148-154
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• 2008

In a ubiquitous computing environment, a ubiquitous smart space is required to help devices provide intelligent services. The smart space embedded with mobile devices should have the capabilities of collecting data and refining the data to contact. Unfortunately, the context information in a ubiquitous smart space has many ambiguous characteristics. Therefore, it is necessary to adapt a standard taxonomy for contact information in the smart space and to implement an inference technique of the context information based on taxonomy. Rule-based inference engine, such as CLIPS, Jess, was employed for providing situation-aware services. However, it is difficult for these engines to be used in resource limited mobile devices. In this paper, we propose a light-weight inference engine providing autonomous situation aware services in mobile environment. It can be utilized for personal mobile devices tuck as mobile phone, PMP and navigation. It can also support both generalized rules and specialized rules as using hierarchical taxonomy information.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).

• Journal of Information Management
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• v.37 no.2
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• pp.109-136
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• 2006

The development of Semantic Web basically requires knowledge which is induced by the formalization and semantization of information, and thus ontology should be introduced as a knowledgization tool. URI(Uniform Resource Identifier) is an indispensible scheme to uniquely indicate individuals on ontology. However, it is difficult to find the use cases of identifiers or URIs in real data sets including science & technology publications. This paper describes the method to construct, manage, and serve reference information based on URI which is a crucial component on establishing national R&D reference information ontology. We expect the reference information which was acquired from about 7,000 proceeding papers would be adopted to Semantic Web applications such as researcher network analysis and outcome statistics.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.551-561
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• 2016

The purpose of this study is to investigate both theoretically and empirically the roles of models in abductive reasoning for scientific problem solving. The context of the study is design-based research the goal of which is to develop inquiry learning programs in the domain of earth science, and the current article dealt with an early process of redesigning an abductive inquiry activity in geology. In the theoretical study, an extensive review was conducted with the literature addressing abduction and modeling together as research methods characterizing earth science. The result led to a tentative scheme for modeling-based abductive inference, which represented relationships among evidence, resource models, and explanatory models. This scheme was improved by the empirical study in which experts' reasoning for solving a geological problem was analyzed. The new scheme included the roles of critical evidence, critical resource models, and a scientifically sound explanatory model. Pedagogical implications for the support of student reasoning in modeling-based abductive inquiry in earth science was discussed.

• Archives of design research
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• v.15 no.3
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• pp.157-166
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• 2002

This research verified advertising effect according to ad information format and layout based on resource-matching theory. Existing researches suggested inconsistent results that it's effective to design advertising easy to understand by using factual information presentation and integrated layout or it's effective do design difficult advertising to understand by using explanatory information presestation and separate layout. The result of this study suggest that advertising effect by advertising design style is different according to situational elements such as motivation level of information processing and brand hierarchy. The results show that, in the high prestige brand, easily designed advertising using factual information presentation and integrated layout is more effective because consumers make favorable inference by remained cognitive resources. Contrary to this in the low prestige brand, not easily designed advertising using explanatory information and separate layout is more effective because consumers have no remained cognitive resources so that they concentrate on advertising itself.