• Safety and Health at Work
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• v.14 no.2
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• pp.163-173
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• 2023

In many industrial sectors, workers are exposed to manufactured or unintentionally emitted airborne nanoparticles (NPs). To develop prevention and enhance knowledge surrounding exposure, it has become crucial to achieve a consensus on how to assess exposure to airborne NPs by inhalation in the workplace. Here, we review the literature presenting recommendations on assessing occupational exposure to NPs. The 23 distinct strategies retained were analyzed in terms of the following points: target NPs, objectives, steps, "measurement strategy" (instruments, physicochemical analysis, and data processing), "contextual information" presented, and "work activity" analysis. The robustness (consistency of information) and practical aspects (detailed methodology) of each strategy were estimated. The objectives and methodological steps varied, as did the measurement techniques. Strategies were essentially based on NPs measurement, but improvements could be made to better account for "contextual information" and "work activity". Based on this review, recommendations for an operational strategy were formulated, integrating the work activity with the measurement to provide a more complete assessment of situations leading to airborne NP exposure. These recommendations can be used with the objective of producing homogeneous exposure data for epidemiological purposes and to help improve prevention strategies.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.76-88
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• 2015

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.86-87
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• 2008

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• English Language & Literature Teaching
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• v.9 no.spc
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• pp.149-165
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• 2003

This paper is aimed at the investigation of pragmatic contributions to the identification of explicatures. An explicature is the result of fleshing out the semantic representation of an utterance. The basic assumption of the paper is that the process of the developing the semantic representation into an explicature depends heavily on contextual information. Therefore, we are concerned with the way in which hearers use contextual information to flesh rut or develop the semantic representation of an utterance. The identification of explicatures includes both the recovery of the proposition expressed and the recovery of what we called higher-level explicatures. There are three subtasks involved in the recovery of the proposition expressed: reference assignment disambiguation and enrichment On the other hand, there are two subtasks involved in the recovery of higher-level explicatures: attitudes and speech acts.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.23-41
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• 2004

This paper proposes an empirical evidence about contextual factors which determine perceived business performances of small firms resulted from IT investment. In this paper, small firms are defined as firms of which total employees are below fifty. These small firms account for 95% of total number of private companies in Korea. We used a perceived IT performance model based on Balanced Scorecard framework to evaluate IT performance of small firms. And data were collected by Web and e-mail survey method with multiple screening. Statistical results show that business performance of small firms are differentiated in terms of firm size, location, longevity, age of owner, education level of owner, while industry sector, profitability, sex of owner don't make significant differences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.248-268
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• 2018

The performance of smartphone applications are usually constrained in user interactions due to resource limitation and it promises great opportunities to improve the performance by exploring the smartphone built-in and embedded sensing techniques. However, heterogeneity in techniques, semantic gap between sensor data and usable context, and complexity of contextual situations keep the techniques from seamless integration. Relevant studies mainly focus on feasibility demonstration of emerging sensing techniques, which rarely address both general architectures and comprehensive technical solutions. Based on a proposed functional model, this investigation provides a general architecture to deal with the dynamic context for context-aware automation and decision support. In order to take advantage of the built-in sensors to improve the performance of mobile applications, an ontology-based method is employed for context modelling, linguistic variables are used for heterogeneous context presentation, and semantic distance-based rule matching is employed to customise functions to the contextual situations. A case study on mobile application authentication is conducted with smartphone built-in hardware modules. The results demonstrate the feasibility of the proposed solutions and their effectiveness in improving operational efficiency.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.128-136
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• 2007

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.125-134
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• 2022

The development of information communication and artificial intelligence technology requires the intelligent command and control (C2) system for Korean military, and various studies are attempted to achieve it. In particular, as a volume ofinformation in the C2 workflow increases exponentially, this study pays attention to the collaborative filtering (CF) and recommendation systems (RS) that can provide the essential information for the users of the C2 system has been developed. The RS performing information filtering in the C2 system should provide an explanatory recommendation and consider the context of the tasks and users. In this paper, we propose a contextual pre-filtering CARS framework that recommends information in the C2 workflow. The proposed framework consists of four components: 1) contextual pre-filtering that filters data in advance based on the context and relationship of the users, 2) feature selection to overcome the data sparseness that is a weak point for the CF, 3) the proposed CF with the features distances between the users used to calculate user similarity, and 4) rule-based post filtering to reflect user preferences. In order to evaluate the superiority of this study, various distance methods of the existing CF method were compared to the proposed framework with two experimental datasets in real-world. As a result of comparative experiments, it was shown that the proposed framework was superior in terms of MAE, MSE, and MSLE.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.125-151
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• 2016

In recent years, O2O (Online to Offline) services get a lot of attention to improve the trust in online shopping and minimize the inconvenience and the cost burden in offline shopping as the number of consumers, who do not show concern about the purchase platform like online or offline, increases. Even though the services have been getting the spotlight as a strong business platform for next generation commerce, there have been only a few studies on the O2O services. The purpose of this research is to investigate the factors which affect the consumer's intention to use location-based O2O services. The study is based on VAM (Value-based Adoption Model) which is able to analyze those factors from the aspects of benefit and sacrifice. We used the partial least squares (PLS) method for empirical analysis, and the result shows that contextual offers, instant connectivity, webrooming and economic efficiency, which fall under the benefit, affect perceived value positively while annoyance and face consciousness, which fall under the sacrifice, do not affect perceived value significantly. In addition, contextual offers and instant connectivity affect trust positively. Location accuracy, which falls under the benefit of location-based O2O service, do not significantly affect perceived value and trust while security risk affects trust and use intention negatively. It appears that trust affects perceived value and use intention positively.