• Smart Structures and Systems
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• v.23 no.6
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• pp.683-693
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• 2019

In August 2012, during the passage of the typhoon Haikui (1211), large amplitude vibrations were observed on long hangers of the Xihoumen suspension Bridge, which destroyed a few viscoelastic dampers originally installed to connect a pair of hanger ropes transversely. The purpose of this study is to identify the cause of vibration and to develop countermeasures against vibration. Field measurements have been conducted in order to correlate the wind and vibration characteristics of hangers. Furthermore, a replica aeroelastic model of prototype hangers consisting of four parallel ropes was used to study the aeroelastic behavior of hanger ropes and to examine the effect of the rigid spacers on vibration mitigation. It is shown that the downstream hanger rope experiences the most violent elliptical vibration for certain wind direction, and the vibration is mainly attributed to wake interference of parallel hanger ropes. Based on wind tunnel tests and field validation, it is confirmed that four rigid spacers placed vertically at equal intervals are sufficient to suppress the wake-induced vibrations. Since the deployment of spacers on hangers, server hanger vibrations and clash of hanger ropes are never observed.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.269-281
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• 2019

This work addresses a three-dimensional nonlinear structural analysis of the constructive phases of a cable-stayed segmental concrete bridge using The Finite Element Method through ANSYS, version 14.5. New subroutines have been added to ANSYS via its UPF customization tool to implement viscoelastoplastic constitutive equations with cracking capability to model concrete's structural behavior. This numerical implementation allowed the use of three-dimensional twenty-node quadratic elements (SOLID186) with the Element-Embedded Rebar model option (REINF264), conducting to a fast and efficient solution. These advantages are of fundamental importance when large structures, such as bridges, are modeled, since an increasing number of finite elements is demanded. After validating the subroutines, the bridge located in Rio de Janeiro, Brazil, and known as "Ponte do Saber" (Bridge of Knowledge, in Portuguese), has been numerically modeled, simulating each of the constructive phases of the bridge. Additionally, the data obtained numerically is compared with the field data collected from monitoring conducted during the construction of the bridge, showing good agreement.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• Structural Monitoring and Maintenance
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• v.6 no.1
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• pp.19-32
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• 2019

One way to provide safe buildings and to protect tenants from the terrorist attacks that have been increasing in the world is to study the behavior of buildings members after being exposed to dynamic loads. Buildings behaviour after being exposed to attacks inspired researchers all around the world to investigate the effect of impact loads on buildings members like slabs and to deeply study the properties of High Performance Concrete. HPC is well-known in its high performance and resistance to dynamic loads when it is compared with normal weight concrete. Therefore, the aim of this paper is finding out the impact of dynamic loads on RPC slabs' flexural capacity, serviceability loads, and failure type. For that purpose and to get answers for these questions, three concrete slabs with 0.5, 1, and 2% steel fiber contents were experimentally tested. The tests results showed that the content of steel fiber plays the key role in specifying the static capacity of concrete slabs after being dynamically loaded, and increasing the content of steel fiber led to improving the static loading capacity, decreased the cracks numbers and widths at the same time, and provided a safer environment for the buildings residents.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.9
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• pp.1332-1339
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• 2019

Heat stress exerts a substantial effect on dairy production. The temperature and humidity index (THI) is widely used to assess heat stress in dairy operations. Herein, we review the effects of high temperature and humidity on body temperature, feed intake, milk production, follicle development, estrous behavior, and pregnancy in dairy cows. Analyses of the effects of THI on dairy production have shown that body temperature is an important physiological parameter in the evaluation of the health state of dairy cows. Although THI is an important environmental index and can help to infer the degree of heat stress, it does not reflect the physiological changes experienced by dairy cows undergoing heat stress. However, the simultaneous measurement of THI and physiological indexes (e.g., body temperature) would be very useful for improving dairy production. The successful development of automatic detection techniques makes it possible to combine THI with other physiological indexes (i.e., body temperature and activity), which could help us to comprehensively evaluate heat stress in dairy cows and provide important technical support to effectively prevent heat stress.

• ETRI Journal
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• v.41 no.4
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.52-71
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• 2019

An insider threat is a threat that comes from people within the organization being attacked. It can be described as a function of the motivation, opportunity, and capability of the insider. Compared to managing the dimensions of opportunity and capability, assessing one's motivation in committing malicious acts poses more challenges to organizations because it usually involves a more obtrusive process of psychological examination. The existing body of research in psycholinguistics suggests that automated text analysis of electronic communications can be an alternative for predicting and detecting insider threat through unobtrusive behavior monitoring. However, a major challenge in employing this approach is that it is difficult to minimize the risk of missing any potential threat while maintaining an acceptable false alarm rate. To deal with the trade-off between the risk of missed catches and the false alarm rate, we propose a unified psycholinguistic framework that consolidates multiple text analyzers to carry out sentiment analysis, emotion analysis, and topic modeling on electronic communications for unobtrusive psychological assessment. The user scenarios presented in this paper demonstrated how the trade-off issue can be attenuated with different text analyzers working collaboratively to provide more comprehensive summaries of users' psychological states.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.5
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• pp.804-824
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• 2021

This study qualitatively explored the case of users utilizing multiple accounts on one social network service to create their own multiple spaces and different personas. The purpose of the study was to understand the behavior of people who use multiple accounts to express their identity online using Carl Jung's personality theory. We used in-depth interviews and the Zaltman metaphor elicitation technique (ZMET), targeting 19 people in their 20s and 30s who use more than one personal account on Instagram. Creating a shared consensus map using the configuration concept of ZMET derived six personas in relation to Instagram accounts. The motivations for the respondents' self-presentation associated with their personas and self-presentation types shown on Instagram were analyzed in terms of persona and fashion and subdivided into five dimensions: relationship management strategic presentation, self-monitoring presentation, competence demonstration presentation, anonymous presentation, and persona-centered presentation. Each respondent's persona and self-presentation formed by the Instagram account was analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4408-4428
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• 2019

Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.