• 아태비즈니스연구
• /
• 제11권4호
• /
• pp.1-19
• /
• 2020

Purpose - This study analyzes the intertemporal patterns in personnel decisions made between a supervisor and a subordinate to understand potential supervisor bias in the decisions. A correlation between the current and the most recent personnel decisions made for a subordinate by a current supervisor captures certain relationship-embedded and time-invariant factors in effect. The characteristics speak to the nature of a supervisor bias arising from a relationship, or favoritism. Design/methodology/approach - This study manually collects the executive profile data from annual reports of key Samsung Group affiliates and compile a longitudinal sample of 3,675 executive-years. It mainly explores the logistic regression analysis. Findings - The study finds that a supervisor' previous promotion award to a subordinate does not improve but decreases the likelihood of promotions in ensuing years, suggesting the containment of favoritism; and that the time since the last promotion award to a subordinate by the current supervisor increases the likelihood of both promotions and dismissals of the subordinate. Research implications or Originality - The findings are generally consistent with the theory suggesting that incentive schemes that align interests between an individual and an organization will contain the form of a supervisor bias.

• Structural Monitoring and Maintenance
• /
• 제8권4호
• /
• pp.379-402
• /
• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Advances in aircraft and spacecraft science
• /
• 제9권3호
• /
• pp.263-278
• /
• 2022

The time-varying structural reliability of an aeroelastic launch vehicle subjected to stochastic parameters is investigated. The launch vehicle structure is under the combined action of several stochastic loads that include aerodynamics, thrust as well as internal combustion pressure. The launch vehicle's main body structural flexibility is modeled via the normal mode shapes of a free-free Euler beam, where the aerodynamic loadings on the vehicle are due to force on each incremental section of the vehicle. The rigid and elastic coupled nonlinear equations of motion are derived following the Lagrangian approach that results in a complete aeroelastic simulation for the prediction of the instantaneous launch vehicle rigid-body motion as well as the body elastic deformations. Reliability analysis has been performed based on two distinct limit state functions, defined as the maximum launch vehicle tip elastic deformation and also the maximum allowable stress occurring along the launch vehicle total length. In this fashion, the time-dependent reliability problem can be converted into an equivalent time-invariant reliability problem. Subsequently, the first-order reliability method, as well as the Monte Carlo simulation schemes, are employed to determine and verify the aeroelastic launch vehicle dynamic failure probability for a given flight time.

• Advances in materials Research
• /
• 제11권3호
• /
• pp.171-190
• /
• 2022

In this paper, the problem of interfacial stresses in steel cantilever beams strengthened with bonded composite laminates is analyzed using linear elastic theory. The analysis is based on the deformation compatibility approach, where both the shear and normal stresses are assumed to be invariant across the adhesive layer thickness. The original study in this paper carried out an analytical solution to estimate shear and peel-off stresses, as, interfacial stress analysis concentration under the uniformly distributed load and shear lag deformation. The theoretical prediction is compared with authors solutions from numerous researches. This phenomenon of deformation of the members, which gives probably approach on the study of interface of the reinforced structures, is called "shear lag effect". The resolution in this paper shows that the shear stress and the normal stress are significant and, are concentrated at the end of the composite plate of reinforcement, called "edge effect". A parametric study is carried out to show the effects of the variables of design and the physical properties of materials. This research is helpful for the understanding on mechanical behaviour of the interface and design of such structures.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권12호
• /
• pp.3868-3888
• /
• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Communications for Statistical Applications and Methods
• /
• 제30권1호
• /
• pp.37-48
• /
• 2023

Shape analysis is a method for measuring, describing and comparing the shape of objects in geometric space. An important aspect is to obtain Procrustes distance based on least square method. We note that the shape is all the geometrical information that remains when location, scale and rotational effects are filtered out from an object. However, and unfortunately, when we cannot measure some landmarks which are some biologically or geometrically meaningful points of any object, it is not possible to measure the variation of all shapes of an object, including that of the incomplete object. Hence, we need to replace the missing landmarks. In particular, Albers and Gower (2010) studied the missing rows of configurations in Procrustes analysis. They noted that the convergence of their approach can be quite slow. In this study, alternatively, we derive an algorithm for estimating the missing landmarks based on the pre-shapes. The pre-shape is invariant under the location and scaling of the original configuration with the centroid size of the pre-shape being one. Therefore we expect that we can reduce the amount of total computing time for obtaining the estimate of the missing landmarks.

• Advances in materials Research
• /
• 제11권2호
• /
• pp.91-109
• /
• 2022

This paper presents a closed-form higher-order analysis of interfacial shear stresses in RC continuous beams strengthened with bonded prestressed laminates. For retrofitting reinforced concrete continuous beams is to bond fiber reinforced prestressed composite plates to their tensile faces. An important failure mode of such plated beams is the debonding of the composite plates from the concrete due to high level of stress concentration in the adhesive at the ends of the composite plate. The model is based on equilibrium and deformations compatibility requirements in and all parts of the strengthened beam, where both the shear and normal stresses are assumed to be invariant across the adhesive layer thickness. In the present theoretical analysis, the adherend shear deformations are taken into account by assuming a parabolic shear stress through the thickness of both the RC continuous beams strengthened with bonded prestressed laminates. The theoretical predictions are compared with other existing solutions. A parametric study has been conducted to investigate the sensitivity of interface behavior to parameters such as laminate stiffness and the thickness of the laminate where all were found to have a marked effect on the magnitude of maximum shear and normal stress in the composite member.

• Steel and Composite Structures
• /
• 제49권2호
• /
• pp.125-141
• /
• 2023

Firstly, a new kind of prismatic tensegrity structures with redundant cables is defined, the topology, geometry and forming conditions of which are introduced further. The development of its mechanical properties including self-stress states and structural stiffness with the increment of the twist angle is also investigated carefully. Combined with the topology of this kind of structures, a reasonable erection scheme is proposed, in which some temporary lifting points need to be set and two groups of vertical cables are tensioned in batches. Then, a simplified dynamic relaxation method is employed to track the erection process inversely, which aims to predict each intermediate equilibrium state during the construction, and give the key structural parameters that can effectively guide the construction. The removal of the active cables, the relaxation or tension of the passive cables are simulated by controlling their axial stiffness, so that the structural composition as well as the serial numbers of the elements always keep invariant regardless of the withdrawal of the slack cables. The whole analysis process is clear in concept, simple to implement and easy to popularize. Finally, several examples are given to verify the practicability and effectiveness of the proposed method further.

• ETRI Journal
• /
• 제45권6호
• /
• pp.1022-1034
• /
• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• 스마트미디어저널
• /
• 제13권2호
• /
• pp.129-135
• /
• 2024

이 연구에서는 MAP-Elites 알고리즘의 연속 최적화 성능을 향상한 새로운 접근법을 제안한다. 기존의 자기 참조 MAP-Elites 알고리즘은 차분 진화 알고리즘의 "DE/rand/1/bin" 연산자를 사용했는데, 이 연산자는 회전 불변이 아니라서 각 변수 간의 상관관계가 높은 경우 성능이 감소하는 문제가 존재한다. 제안하는 알고리즘은 "DE/rand/1/bin" 연산자 대신에 "DE/current-to-rand/1" 연산자를 사용한다. 이 연산자는 회전 불변성을 가지므로 각 변수 간의 상관관계가 높은 분리 불가능 최적화 문제에서도 강건한 성능을 보장할 수 있다. 실험 결과, 제안하는 알고리즘이 비교 알고리즘들에 비해 높은 성능을 발휘함을 확인했다.