• Journal of Information Processing Systems
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• v.17 no.1
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• pp.89-106
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• 2021

Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalance an imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples for learning classifiers. Safe sample screening can identify a part of non-informative samples and retain informative samples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe Sample Screening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via a Weighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class, and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informative minority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. The experimental results show that Re-SSS can effectively improve the classification performance of imbalanced classification problems.

• Smart Structures and Systems
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• v.24 no.1
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• pp.141-155
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• 2019

This work evaluates the influence of negative stiffness on the performances of various vehicle suspension systems, and proposes a re-centering negative stiffness device (NSD). The re-centering NSD consists of a passive magnetic negative stiffness spring and a positioning shaft with a re-centering function. The former produces negative stiffness control forces, and the latter prevents the amplification of static spring deflection. The numerical simulations reveal that negative stiffness can improve the ride comfort of a vehicle without affecting its road holding abilities for either passive or semi-active suspension systems. In general, the improvement degree of ride comfort increases as negative stiffness increases. For passive suspension system, negative stiffness brings in negative stiffness feature in the control forces, which is helpful for the ride comfort of a vehicle. For semi-active suspensions, negative stiffness can alleviate the impact of clipped damping in semi-active dampers, and thus the ride comfort of a vehicle can be improved.

• Smart Structures and Systems
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• v.21 no.5
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• pp.653-668
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• 2018

This work proposes and investigates re-centering negative stiffness dampers (NSDs) for vibration suppression in high-speed trains. The merit of the negative stiffness feature is demonstrated by active controllers on a high-speed train. This merit inspires the replacement of active controllers with re-centering NSDs, which are more reliable and robust than active controllers. The proposed damper design consists of a passive magnetic negative stiffness spring and a semi-active positioning shaft for re-centering function. The former produces negative stiffness control forces, and the latter prevents the amplification of quasi-static spring deflection. Numerical investigations verify that the proposed re-centering NSD can improve ride comfort significantly without amplifying spring deflection.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Smart Structures and Systems
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• v.25 no.1
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• pp.105-109
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• 2020

The authors address the problem of ameliorating or updating existing educational buildings. This building typology is quite sensitive to social and media pressure, mainly when accidents have occurred nearby. When a building is classified as unsatisfactory, the current code requirements oblige one to re-design the building with significant penalty factors in the resistance values. Often the only solution is to destroy the existing facility and to build a new one. When attempting to preserve the existing building, higher levels of safety are demanded by the society and this can only be achieved by innovative system architectures. The authors propose and discuss a prototype that can be easily adopted to retrofit small educational buildings as the ones common in small municipalities. The higher performance is pursued by a special design of the control scheme, with new control devices and special control laws.

• Smart Structures and Systems
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• v.13 no.2
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• pp.219-233
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• 2014

In a companion paper, Pasala and Nagarajaiah analytically and experimentally validate the Adaptive Length Pendulum Smart Tuned Mass Damper (ALP-STMD) on a primary structure (2 story steel structure) whose frequencies are time invariant (Pasala and Nagarajaiah 2012). In this paper, the ALP-STMD effectiveness on a primary structure whose frequencies are time varying is studied experimentally. This study experimentally validates the ability of an ALP-STMD to adequately control a structural system in the presence of real time changes in primary stiffness that are detected by a real time observer based system identification. The experiments implement the newly developed Adaptive Length Pendulum Smart Tuned Mass Damper (ALP-STMD) which was first introduced and developed by Nagarajaiah (2009), Nagarajaiah and Pasala (2010) and Nagarajaiah et al. (2010). The ALP-STMD employs a mass pendulum of variable length which can be tuned in real time to the parameters of the system using sensor feedback. The tuning action is made possible by applying a current to a shape memory alloy wire changing the effective length that supports the damper mass assembly in real time. Once a stiffness change in the structural system is detected by an open loop observer, the ALP-STMD is re-tuned to the modified system parameters which successfully reduce the response of the primary system. Significant performance improvement is illustrated for the stiffness modified system, which undergoes the re-tuning adaptation, when compared to the stiffness modified system without adaptive re-tuning.

• Smart Structures and Systems
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• v.5 no.4
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• pp.369-379
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• 2009

The sudden collapse of Interstate 35 Bridge in Minneapolis gave a wake-up call to US municipalities to re-evaluate aging bridges. In this situation, structural health monitoring (SHM) technology can provide the essential help needed for monitoring and maintaining the nation's infrastructure. Monitoring long span bridges such as cable-stayed bridges effectively requires the use of a large number of sensors. In this article, we introduce a probabilistic approach to identify optimal locations of sensors to enhance damage detection. Probability distribution functions are established using an artificial neural network trained using a priori knowledge of damage locations. The optimal number of sensors is identified using multi-objective optimization that simultaneously considers information entropy and sensor cost-objective functions. Luling Bridge, a cable-stayed bridge over the Mississippi River, is selected as a case study to demonstrate the efficiency of the proposed approach.

• Smart Structures and Systems
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• v.8 no.4
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• pp.399-412
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• 2011

This study compares the performance of two smart isolation systems that utilize superelastic shape memory alloys (SMAs) for seismic protection of bridges using energy balance concepts. The first isolation system is a SMA/rubber-based isolation system (SRB-IS) and consists of a laminated rubber bearing that decouples the superstructure from the bridge piers and a SMA device that provides additional energy dissipation and re-centering capacity. The second isolation system, named as superelastic-friction base isolator (S-FBI), combines the superelastic SMAs with a flat steel-Teflon bearing rather than a laminated rubber bearing. Seismic energy equations of a bridge structure with SMA-based isolation systems are established by absolute and relative energy balance formulations. Nonlinear time history analyses are performed in order to assess the effectiveness of the isolation systems and to compare their performance. The program RSPMatch 2005 is employed to generate spectrum compatible ground motions that are used in time history analyses of the isolated bridge. Results indicate that SRB-IS produces higher seismic input energy, recoverable energy and base shears as compared to the S-FBI system. Also, it is shown that combining superelastic SMAs with a sliding bearing rather than rubber bearing significantly reduce the amount of the required SMA material.

• Smart Structures and Systems
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• v.10 no.1
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• pp.1-14
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• 2012

Given an object, its positioning in the space is a main concern in structural monitoring and a required feedback in structural health monitoring, structural control and robotics. In addition, to make the sensor unit wireless is a crucial issue for advanced applications. This paper deals with the exploitation of wireless transmission technology to long-term monitoring GPS (Global Positioning System) receivers - like the Leica GMX 902 and the Leica GRX 1200-pro. These GPS receivers consist of five parts: antenna, receiver, user client computer, interface and power supply. The antenna is mounted on the object to be monitored and is connected with the receiver by a coaxial-cable through which the radio frequency signals are transmitted. The receiver unit acquires, tracks and demodulates the satellite signals and provides, through an interface which in this paper is made wireless, the resulting GPS raw data to the user client computer for being further processed by a suitable positioning algorithm. The power supply reaches the computer by a wired link, while the other modules rely on batteries re-charged by power harvesting devices. Two wireless transmission systems, the 24XStream and the CC1110, are applied to replace the cable transmission between the receiver and the user client computer which up to now was the only market offer. To verify the performance and the reliability of this wireless transmission system, some experiments are conducted. The results show a successful cable replacement.

• Smart Structures and Systems
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• v.15 no.1
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• pp.41-55
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• 2015

This study proposes a laser based impedance measurement system and impedance based pipe corrosion and bolt-loosening monitoring techniques under temperature variations. For impedance measurement, the laser based impedance measurement system is optimized and adopted in this paper. First, a modulated laser beam is radiated to a photodiode, converting the laser beam into an electric signal. Then, the electric signal is applied to a MFC transducer attached on a target structure for ultrasonic excitation. The corresponding impedance signals are measured, re-converted into a laser beam, and radiated back to the other photodiode located in a data interrogator. The transmitted impedance signals are treated with an outlier analysis using generalized extreme value (GEV) statistics to reliably signal off structural damage. Validation of the proposed technique is carried out to detect corrosion and bolt-loosening in lab-scale carbon steel elbow pipes under varying temperatures. It has been demonstrated that the proposed technique has a potential to be used for structural health monitoring (SHM) of pipe structures.