• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• Agribusiness and Information Management
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• v.7 no.2
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• pp.27-37
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• 2015

This study was carried out with the goal of improving accessibility for farmers in record management. The pre-survey for farm records found that the main reasons for lack of records were "no need" and "no time" to keep such records. Based on the results of the pre-survey of smartphone-based farm recordkeeping, the major composition articles were management books, daily work logs, analysis books and additional books compiled by setting log in, notice and suggestions. The number of smartphone-based farm record books was 4,430 nationwide. We also updated farms keeping records on seven occasions for user convenience. Regional differences in the utilization ratio of record books showed that continuous promotion and education aimed at users were key points of success for farm recordkeeping books.

• Smart Structures and Systems
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• v.14 no.2
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

• Smart Structures and Systems
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• v.24 no.6
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• pp.683-692
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• 2019

Traditional damage detection methods for nonlinear structures are often based on simplified models, such as the mass-spring-damper and shear-building models, which are insufficient for predicting the vibration responses of a real structure. Conventional global nonlinear finite element model updating methods are computationally intensive and time consuming. Thus, they cannot be applied to practical structures. A decentralized approach for identifying the nonlinear material parameters is proposed in this study. With this technique, a structure is divided into several small zones on the basis of its structural configuration. The unknown material parameters and measured vibration responses are then divided into several subsets accordingly. The structural parameters of each subset are then updated using the vibration responses of the subset with the Newton-successive-over-relaxation (SOR) method. A reinforced concrete and steel frame structure subjected to earthquake loading is used to verify the effectiveness and accuracy of the proposed method. The parameters in the material constitutive model, such as compressive strength, initial tangent stiffness and yielding stress, are identified accurately and efficiently compared with the global nonlinear model updating approach.

• Korean Journal of Plant Resources
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• v.36 no.3
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• pp.219-224
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• 2023

Melilotus dentatus (Waldst. & Kit.) Desf. is an invasive plant native to Europe, Russia, Uzbekistan, Mongolia and China. It was introduced to North America to create grazing areas for livestock; its growth has since become uncontrollable. Melilotus dentatus is now found in Namyang-eup, Hwaseong-si, Gyeonggi-do, South Korea as an alien plant. This species is morphologically similar to Melilotus suaveolens Ledeb. and Melilotus officinalis (L.) Lam. M. dentatus differs from other species based on its oblong leaflets, distinct teeth, two ovules, surface of the legume that is minutely reticulate-nerved or smooth, and seeds that are dark green to greenish brown. Here, we have provided a detailed description and photographs of the alien species M. dentatus as well as an updated key to five Melilotus taxa studied, which will help prepare a continuous monitoring and management plan.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.24-25
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• 2015

To realize the global standard of the assembly facility, one of key point is an influence of local characteristic. The global standardized information could be a useful input, especially at the beginning stage of the project. The content should be carefully checked against local requirements, in order to develop an Assembly Facility design to meet local regulations & characteristics. The level of input information required in each step is different depend on regions & countries. At an execution stage of the project, the global standard input needs to be updated as customized final input for the construction. There will be many issues to be clarified and coordinated so that the Assembly facilities meet the final requirements for the production technology. As a result, it is important to understand the timing to use the global standard information and the contents what is needed to adapt to the local characteristics. And close collaboration between production technology team and facility team is always required.

• IMMUNE NETWORK
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• v.24 no.1
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• pp.2.1-2.24
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• 2024

Studies over the last 2 decades have identified IL-17 and IL-21 as key cytokines in the modulation of a wide range of immune responses. IL-17 serves as a critical defender against bacterial and fungal pathogens, while maintaining symbiotic relationships with commensal microbiota. However, alterations in its levels can lead to chronic inflammation and autoimmunity. IL-21, on the other hand, bridges the adaptive and innate immune responses, and its imbalance is implicated in autoimmune diseases and cancer, highlighting its important role in both health and disease. Delving into the intricacies of these cytokines not only opens new avenues for understanding the immune system, but also promises innovative advances in the development of therapeutic strategies for numerous diseases. In this review, we will discuss an updated view of the immunobiology and therapeutic potential of IL-17 and IL-21.

• ETRI Journal
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• v.46 no.1
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• pp.11-21
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• 2024

We focus on open-domain question-answering tasks that involve a chain-of-reasoning, which are primarily implemented using large language models. With an emphasis on cost-effectiveness, we designed EffiChainQA, an architecture centered on the use of small language models. We employed a retrieval-based language model to address the limitations of large language models, such as the hallucination issue and the lack of updated knowledge. To enhance reasoning capabilities, we introduced a question decomposer that leverages a generative language model and serves as a key component in the chain-of-reasoning process. To generate training data for our question decomposer, we leveraged ChatGPT, which is known for its data augmentation ability. Comprehensive experiments were conducted using the HotpotQA dataset. Our method outperformed several established approaches, including the Chain-of-Thoughts approach, which is based on large language models. Moreover, our results are on par with those of state-of-the-art Retrieve-then-Read methods that utilize large language models.

• Smart Structures and Systems
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• v.23 no.3
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• pp.263-278
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• 2019

Moving train load parameters, including train speed, axle spacing, gross train weight and axle weights, are identified based on strain-monitoring data. In this paper, according to influence line theory, the classic moving force identification method is enhanced to handle time-varying velocity of the train. First, the moments that the axles move through a set of fixed points are identified from a series of pulses extracted from the second derivative of the structural strain response. Subsequently, the train speed and axle spacing are identified. In addition, based on the fact that the integral area of the structural strain response is a constant under a unit force at a unit speed, the gross train weight can be obtained from the integral area of the measured strain response. Meanwhile, the corrected second derivative peak values, in which the effect of time-varying velocity is eliminated, are selected to distribute the gross train weight. Hence the axle weights could be identified. Afterwards, numerical simulations are employed to verify the proposed method and investigate the effect of the sampling frequency on the identification accuracy. Eventually, the method is verified using the real-time strain data of a continuous steel truss railway bridge. Results show that train speed, axle spacing and gross train weight can be accurately identified in the time domain. However, only the approximate values of the axle weights could be obtained with the updated method. The identified results can provide reliable reference for determining fatigue deterioration and predicting the remaining service life of railway bridges.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8279-8285
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• 2014

A meta-analysis incorporating 34 case-control studies from 19 articles involving 12,197 cases and 13,488 controls was conducted to assess the effects of three genetic variants of Toll-like receptor 9 (TLR9): rs187084, rs352140, and rs5743836. Studies on associations between TLR9 polymorphisms and cancer risk were systematically searched in electronic databases. The reported odds ratios (OR) and 95% confidence intervals (CI) were pooled to assess the strength of any associations. The results showed that the rs187084 polymorphism was significantly associated with an increased risk of cancer (CC vs TC+TT: OR=1.14, 95% CI=1.02-1.28), specifically cervical cancer (C vs T: OR=1.19, 95% CI=1.05-1.34; TC vs TT: OR=1.32, 95% CI=1.10-1.58; CC vs TT: OR=1.31, 95% CI=1.03-1.68; CC+TC vs TT: OR=1.32, 95% CI=1.11-1.56), and that this association was significantly positive in Caucasians (CC vs. TC+TT: OR=1.18, 95% CI=1.01-1.38). The rs352140 polymorphism had a protective effect on breast cancer (GA vs GG: OR=0.77, 95% CI=0.66-0.89), whereas the rs5743836 polymorphism was likely protective for digestive system cancers (CC+TC vs TT: OR=0.81, 95% CI=0.66-0.98). In conclusion, our results suggest that the rs187084 polymorphism may be associated with an elevated cancer risk, whereas polymorphisms of rs352140 and rs5743836 may play protective roles in the development of breast and digestive system cancers, respectively. From the results of this meta-analysis further large-scale case-control studies are warranted to verify associations between TLR9 polymorphisms and cancer.