• 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.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.249-261
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• 2021

To tranquilize the devastating impact of unnecessary risk-taking behavior of banks towards the economy for maximizing their profits that usually arises due to widely known 'moral-hazard' problem originating from market competition and intensified by bank's limited liability, the banking system is strongly monitored across all countries of the world. The goal of controlling would become more feasible if there exist some self-discipline and motivations which could safeguard the banks' charter value through the mechanism of market discipline. Therefore, our study is aimed to scrutinize the relation between market discipline and charter value of local commercial banks that are registered on the Pakistan Stock Exchange by analyzing a balanced panel data from the year 2007 to 2019. Deposit growth, interbank deposits, and subordinate debt are taken as proxies to measure market discipline whereas Tobin's Q theory is applied for calculating the charter value. Generalized Least Square Regression with Fixed Effect Model is used for evaluation. The outcomes reveal that in the existence of control variables, all proxies of market discipline have a significant positive impact on bank charter value. Our research has important policy implications for monitoring and supervising financial intermediaries for their stability and soundness by offsetting the complications of moral-hazard in the financial systems.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.19-29
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• 2022

Natural user interface (NUI) is used for the natural motion interface without using a specific device or tool like a mouse, keyboards, and pens. Recently, as non-contact sensor-based interaction technologies for recognizing human motion, gestures, voice, and gaze have been actively studied, an environment has been prepared that can provide more diverse contents based on various interaction methods compared to existing methods. However, as the number of sensors device is rapidly increasing, the system using a lot of sensors can suffer from a lack of computational resources. To address this problem, we proposed a real-time multi-device control system for natural interactive platform. In the proposed system, we classified two types of devices as the HC devices such as high-end commercial sensor and the LC devices such astraditional monitoring sensor with low-cost. we adopt each device manager to control efficiently. we demonstrate a proposed system works properly with user behavior such as gestures, motions, gazes, and voices.

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.96-103
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• 2022

Purpose: The survival of organisms critically depends on avoidance responses to life-threatening stimuli. Information about dangerous situations needs to be remembered to produce defensive behavior. To investigate underlying brain regions to process information of danger, manganese-enhanced MRI (MEMRI) was used in olfactory fear-conditioned rats. Materials and Methods: Fear conditioning was conducted in male Sprague-Dawley rats. The animals received nasal injections of manganese chloride solution to monitor brain activation for olfactory information processing. Twenty-four hours after manganese injection, rats were exposed to electric foot shocks with odor cue for one hour. Control rats were exposed to the same odor cue without foot shocks. Forty-eight hours after the conditioning, rats were anesthetized and their brains were scanned with 9.4T MRI. Acquired images were processed and statistical analyses were performed using AFNI. Results: Manganese injection enhanced brain areas involved in olfactory information pathways in T1 weighted images. Rats that received foot shocks showed higher brain activation in the central nucleus of the amygdala, septum, primary motor cortex, and preoptic area. In contrast, control rats displayed greater signals in the orbital cortex and nucleus accumbens. Conclusion: Nasal delivery of manganese solution enhanced olfactory signal pathways in rats. Odor cue paired with foot shocks activated amygdala, the central brain region in fear, and related brain circuits. Use of MEMRI in fear conditioning provides a reliable monitoring technique of brain activation for fear learning.

• Smart Media Journal
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• v.11 no.8
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• pp.84-92
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• 2022

Precision livestock monitoring promises greater management efficiency for farmers and higher welfare standards for animals. Recent studies on video-based animal activity recognition and tracking have shown promising solutions for understanding animal behavior. To achieve that, surveillance cameras are installed diagonally above the barn in a typical cattle farm setup to monitor animals constantly. Under these circumstances, tracking individuals requires addressing challenges such as occlusion and visual appearance, which are the main reasons for track breakage and increased misidentification of animals. This paper presents a framework for multi-cattle tracking in closed barns with appearance and motion models. To overcome the above challenges, we modify the DeepSORT algorithm to achieve higher tracking accuracy by three contributions. First, we reduce the weight of appearance information. Second, we use an Ensemble Kalman Filter to predict the random motion information of cattle. Third, we propose a supplementary matching algorithm that compares the absolute cattle position in the barn to reassign lost tracks. The main idea of the matching algorithm assumes that the number of cattle is fixed in the barn, so the edge of the barn is where new trajectories are most likely to emerge. Experimental results are performed on our dataset collected on two cattle farms. Our algorithm achieves 70.37%, 77.39%, and 81.74% performance on HOTA, AssA, and IDF1, representing an improvement of 1.53%, 4.17%, and 0.96%, respectively, compared to the original method.

• Smart Structures and Systems
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• v.28 no.6
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• pp.761-777
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• 2021

This study presents a numerical investigation on the sensitivity of electromechanical (EM) impedance responses to inner damaged concrete of a prestressed anchorage zone. Firstly, the Ottosen yield criterion is selected to simulate the plasticity behavior of the concrete anchorage zone under the compressive loading. Secondly, several overloading cases are selected to analyze inner damage formations in the concrete of the anchorage zone. Using a finite element (FE) model of the anchorage zone, the relationship between applied forces and stresses is analyzed to illustrate inner plasticity regions in concrete induced by the overloading. Thirdly, EM impedance responses of surface-mounted PZT (lead-zirconate-titanate) sensors are numerically acquired before and after concrete damage occurrence in the anchorage zone. The variation of impedance responses is estimated using the RMSD (root-mean-square-deviation) damage metric to quantify the sensitivity of the signals to inner damaged concrete. Lastly, a novel PZT skin, which can measure impedance signatures in predetermined frequency ranges, is designed for the anchorage zone to sensitively monitor the EM impedance signals of the inner damaged concrete. The feasibility of the proposed method is numerically evaluated for a series of damage cases of the anchorage zone. The results reveal that the proposed impedance-based method is promising for monitoring inner damaged concrete in anchorage zones.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.1
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• pp.41-48
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• 2022

Vehicle vibration was introduced in the time and frequency domains using fast Fourier transform (FFT) analysis. In particular, a vibration mode analysis and characteristics of the frequency response function (FRF) in a sport utility vehicle (SUV) passing over a bump barrier at different speeds was performed systematically. The response behavior of the theoretical acceleration was obtained using a numerical method applied to the forced vibration model. The amplitude and frequency of the external force on the vehicle cause various power spectra with individual intrinsic system frequencies. In this regard, several modes of power spectra were acquired from the spectra and are discussed in this paper. The proposed technique can be used for monitoring the acceleration in a vehicle passing over a bump barrier. To acquire acceleration signals, various experimental runs were performed using the SUV. These acceleration signals were then used to acquire the FRF and to conduct mode analysis. The vehicle characteristics according to the vehicle condition were analyzed using FRF. In addition, the vehicle structural system and bump passing frequencies were discriminated based on their power spectra and other FRF spectra.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.81-105
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• 2022

The use of concrete filled steel tube (CFST) column is widely accepted due to its property of high axial load carrying capacity, more ductility and more resistant to earthquake specially using in bridges and high-rise buildings. The initial imperfection (δ) that produces during casting or fixing causes the reduction in load carrying capacity, this is the reason, experimental capacity is always less then theoretical one. In this research, the effect of δ on load carrying capacity and behavior of concrete filled steel tube (CFST) column have been investigated by numerically simulation of large number of models with different δ and other geometric parameters that include length (L), width (B), steel tube thickness (t), f'_c and f_y. Finite element analysis software ANSYS v18 is used to develop model of SCFST column to evaluate strength capacity, buckling and failure pattern of member which is applied during experimental study under cyclic axial loading. After validation of results, 42 models with different parameters are evaluated to develop empirical equation predicting axial load carrying capacity for different value of δ. Results indicate that empirical equation shows the 0 to 9% error for finite element analysis Forty-two models in comparison with ANSYS results, respectively. Empirical equation can be used for predicting the axial capacity of early estimating the axial capacity of SCFT column including 𝛿.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.1-27
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• 2022

Extensive laboratory tests were conducted to investigate the effect of load amplitude, geogrid position, and number of geogrid layers, thickness of ballast layer and clay stiffness on behavior of reinforced ballast layer and induced strains in geogrid. A half full-scale railway was constructed for carrying out the tests, the model consists of two rails 800 mm in length with three wooden sleepers (900 mm × 10 mm × 10 mm). The ballast was overlying 500 mm thickness clay in two states, soft and stiff state. Laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid. Settlement in ballast and clay, soil pressure and pore water pressure induced in the clay were measured in reinforced and unreinforced ballast cases. It was concluded that the effect of frequency on the settlement ratio is almost constant after 500 cycles. This is due to that the total settlement after 500 cycles, almost reached its peak value, which means that the ballast particles become very close to each other, so the frequency is less effective for high contact particles forces. The average maximum vertical stress and pore water pressure increased with frequency.