• Safety and Health at Work
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• v.10 no.4
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• pp.393-399
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• 2019

Objective: This study investigates the impact of multimorbidity on work through a literature review of longitudinal studies. Methods: A systematic review was carried out in the databases Lilacs, SciELO, PAHO, PubMed/Medline, Scopus, Web of Science, and Cochrane. There were no restrictions regarding the year of publication or language to maximize the identification of relevant literature. The quality of studies was assessed by the protocol STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). Results: An initial database search identified 7522 registries, and at the end of the analysis, 7 manuscripts were included in the review. Several studies have demonstrated direct and indirect impacts of multimorbidity on the health of workers. For this, the number of missed days due to health-related issues was evaluated, as well as the reduction in work productivity of the unhealthy worker, vulnerability of the worker with multimorbidity regarding higher indices of dismissal and recruitment difficulties, and incidence of early retirement and/or receipt of benefits due to disabilities. Conclusions: Multimorbidity has a negative impact on work, with damages to quality of life and work productivity, worsening the absenteeism/presenteeism indices, enhancing the chances of temporary or permanent leaves, and lowering employability and admission of individuals with multimorbidity.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.763-782
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• 2019

This paper proposes a probabilistic document ranking model incorporating term dependencies. Document ranking is a fundamental information retrieval task. The task is to sort documents in a collection according to the relevance to the user query (Qin et al., Information Retrieval Journal, 13, 346-374, 2010). A probabilistic model is a model for computing the conditional probability of the relevance of each document given query. Most of the widely used models assume the term independence because it is challenging to compute the joint probabilities of multiple terms. Words in natural language texts are obviously highly correlated. In this paper, we assume a multinomial distribution model to calculate the relevance probability of a document by considering the dependency structure of words, and propose an information retrieval model to rank a document by estimating the probability with the maximum entropy method. The results of the ranking simulation experiment in various multinomial situations show better retrieval results than a model that assumes the independence of words. The results of document ranking experiments using real-world datasets LETOR OHSUMED also show better retrieval results.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.306-312
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• 2008

In a goal-oriented dialogue, speaker's intention can be approximated by a semantic structure that consists of a pair of a speech act and a concept sequence. Therefore, it is very important to correctly identify the semantic structure of an utterance for implementing an intelligent dialogue system. In this paper, we propose a model to efficiently analyze the semantic structures based on an active teaming method. To reduce the burdens of high-level linguistic analysis, the proposed model only uses morphological features and previous semantic structures as input features. To improve the precisions of semantic structure analysis, the proposed model adopts CRFs(Conditional Random Fields), which show high performances in natural language processing, as an underlying statistical model. In the experiments in a schedule arrangement domain, we found that the proposed model shows similar performances(92.4% in speech act analysis and 89.8% in concept sequence analysis) to the previous models although it uses about a third of training data.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.14-23
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• 1994

In this paper, speech units appropriate for recognition of Korean language have been studied. For better speech recognition, co-articulatory effects within an utterance should be considered in the selection of a recognition unit. One way to model such effects is to use larger units of speech. It has been found that diphone is a good recognition unit because it can model transitional legions explicitly. When diphone is used, stationary phoneme models may be inserted between diphones. Computer simulation for isolated word recognition was done with 7 word database spoken by seven male speakers. Best performance was obtained when transition regions between phonemes were modeled by two-state HMM's and stationary phoneme regions by one-state HMM's excluding /b/, /d/, and /g/. By merging rarely occurring diphone units, the recognition rate was increased from $93.98\%$ to $96.29\%$. In addition, a local interpolation technique was used to smooth a poorly-modeled HMM with a well-trained HMM. With this technique we could get the recognition rate of $97.22\%$ after merging some diphone units.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.233-238
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• 2010

In this paper, we applied the forward and backward error recovery techniques to a reduced instruction set computer (risc) processor to develop two fault-tolerant processors, namely, fetch redundant risc (FRR) processor and a redundancy execute risc (RER) processor. To evaluate the fault-tolerance capability of three target processors, we developed the base risc processor, FRR processor, and RER processor in SystemC hardware description language. We performed fault injection experiment using the three SystemC processor models and the SystemC-based simulation fault injection technique. From the experiments, for the 1-bit transient fault, the failure rate of the FRR, RER, and base risc processor were 1%, 2.8%, and 8.9%, respectively. For the 1-bit permanent fault, the failure rate of the FRR, RER, and base risc processor were 4.3%, 6.5%, and 41%, respectively. As a result, for 1-bit fault, we found that the FRR processor is more reliable among three processors.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.1-12
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• 2005

The XML is emerging as standard language for data exchange on the Web. Therefore a demand of XML Schema(W3C MLL Schema Spec.) that verifies XML document becomes increasing. However, XML Schema has a weak point for design because of its complication despiteof various data and abundant expressiveness. This paper shows a simple way of design for XML Schema using a fundamental means for database design, the Entity-Relationship model. The conversion from the Entity-Relationship model to XML Schema can not be directly on account of discordance between the two models. So we present some algorithms to generate XML Schema from the Entity-Relationship model. The algorithms produce XML Schema codes using a hierarchical view representation. An important objective of this automatic generation is to preserve XML Schema's characteristics such as reusability, global and local ability, ability of expansion and various type changes.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.92-99
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• 2005

In this paper, we present a high-level design methodology applied on a network system-on-a-chip(SOC) using SystemC. The main target of our approach is to get optimum performance parameters for high network address translation(NAT) throughput. The Fast Ethernet media access controller(MAC) and its direct memory access(DMA) controller are modeled with SystemC in transaction level. They are calibrated through the cycle-based measurement of the operation of the real Verilog register transfer language(RTL). The NAT throughput of the model is within $\pm$10% error compared to the output of the real evaluation board. Simulation speed of the model is more than 100 times laster than the RTL. The validated models are used for intensive architecture exploration to find the performance bottleneck in the NAT router.

• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.689-696
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• 2007

It is important issue for software architects to estimate performance of software in the early phase of the development process due to the need to verify non-functional requirements and estimation of performance in various stages of architectural design. In order to analyze performance of software, there are many approaches to translate software architecture represented by Unified Modeling Language, into analytical models. However, in the development of agent-based systems, these approaches ignore or simplify the crucial details of the underlying performance of the agent platform. In this paper, we propose performance prediction methodology for agent based system using formal semantic descriptions, and then, we transform the descriptions into queuing network model which model reflects performance of hardware and software platform. We prove the accuracy of proposed methodology using prototype implementation. The accuracy is summarized at 80%.

• Cartoon and Animation Studies
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• s.49
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• pp.537-561
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• 2017

Machines and robotic structures are questioning existential bases of human beings. They influence our way of thinking and transform our social philosophies and value systems. The same time they keep their fascination ever since. Mechanical technique and skills are symbols for development and hazard at the same time. The attraction of this ambivalence is reviewed in this thesis by having a look at kinetic elements in modern sculpture. The author is focusing on classical sculptural positions that use machines or machinery within sculptural artworks. The historical development is examined and certain examples from the classical modern era are discussed as role models during their time. On this scenario, the portraits of two young contemporary outstanding positions in the field of kinetic art and installation are analyzed and explained. One of the goals was to investigate the eastern and the western language of two artists that are doing artworks in the same field and compare the works and the cultural expressions to each other. Different cultural positions and visual languages become visible due to the research. And the author's final conclusions is, that even in a globalized world there will always be significant local distinguishes remaining.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.109-121
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• 2007

In this study, an imputation model using circular probability distribution was developed in order to overcome problems of missing data from a traffic survey. The existing ad-hoc or heuristic, model-based and algorithm-based imputation techniques were reviewed through previous studies, and then their limitations for imputing missing traffic volume data were revealed. The statistical computing language 'R' was employed for model construction, and a mixture of von Mises probability distribution, which is classified as symmetric, and unimodal circular probability were finally fitted on the basis of traffic volume data at survey stations in urban and rural areas, respectively. The circular probability distribution model largely proved to outperform a dummy variable regression model in regards to various evaluation conditions. It turned out that circular probability distribution models depict circularity of hourly volumes well and are very cost-effective and robust to changes in missing mechanisms.