Application of maximum entropy principle for reliability-based design optimization

The maximum entropy principle (MEP) is used to generate a natural probability distribution among the many possible that have the same moment conditions. The MEP can accommodate higher order moment information and therefore facilitate a higher quality PDF model. The performance of the MEP for PDF estimation is studied by using more than four moments. For the case with four moments, the results are compared with those by the Pearson system. It is observed that as accommodating higher order moment, the estimated PDF converges to the original one. A sensitivity analysis formulation of the failure probability based on the MEP is derived for reliability-based design optimization (RBDO) and the accuracy is compared with that by finite difference method (FDM). Two RBDO examples including a realistic three-dimensional wing design are solved by using the derived sensitivity formula and the MEP-based moment method. The results are compared with other methods such as TR-SQP, FAMM + Pearson system, FFMM + Pearson system in terms of accuracy and efficiency. It is also shown that an improvement in the accuracy by including more moment terms can increase numerical efficiency of optimization for the three-dimensional wing design. The moment method equipped with the MEP is found flexible and well adoptable for reliability analysis and design.     

Incidental Exposure,Selective Exposure,and Political Information Sharing: Integrating Online Exposure Patterns and Expression on Social Media

Political information sharing in social media offers citizens opportunities to engage with news and express their political views, but how do different patterns of online political information exposure, including both incidental and selective exposure, affect sharing? Using two‐wave panel survey data collected in the United States, we examine the relationship between incidental and selective exposure and their consequent links to political information sharing, across different levels of strength of political party affiliation. Our results demonstrate that incidental exposure to counter‐attitudinal information drives stronger partisans to more actively seek out like‐minded political content, which subsequently encourages political information sharing on social media. The results highlight the need to consider both types of political information exposure when modeling citizens' political behavior online.     

Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence. We collectively refer to the convergence of different technology sectors as the internet of blended environment. The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder. An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02% detection accuracy. Furthermore, performance of the proposed approach was compared with various single model (autoencoder)-based network intrusion detection approaches: autoencoder, variational autoencoder, convolutional variational autoencoder, and long short-term memory variational autoencoder. The proposed model outperformed all compared models, demonstrating F1-score improvements of 4.99%, 2.25%, 1.92%, and 3.69%, respectively.     

Estimating plot volume using lidar height and intensity distributional parameters

This study explored the feasibility of height distributional metrics and intensity values extracted from low-density airborne light detection and ranging (lidar) data to estimate plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression analyses were performed using lidar height and intensity distributional metrics. The candidate variables for predicting plot volume were evaluated using three data sets: total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar returns used were corrected by the reference distance. Regression models were developed using each data set, and the first criterion used to select the best models was the corrected Akaike Information Criterion (AIC_c). The use of three data sets was statistically significant at R² = 0.75 (RMSE = 52.17 m³ ha^?1), R² = 0.84 (RMSE = 45.24 m³ ha^?1), and R² = 0.91 (RMSE = 31.48 m³ ha^?1) for total, canopy, and integrated lidar distributional metrics, respectively. Among the three data sets, the integrated lidar metrics-derived model showed the best performance for estimating plot volumes, improving errors up to 42% when compared to the other two data sets. This is attributed to supplementing variables weighted and biased to upper limits in dense plots with more statistical variables that explain the lower limits. In all data sets, intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard error were employed as explanatory variables. The use of intensity variables improved the accuracy of volume estimation in dense forests compared to prior research. Correction of the intensity values contributed up to a maximum of 58% improvement in volume estimation when compared to the use of uncorrected intensity values (R² = 0.78, R² = 0.53, and R² = 0.63 for total, canopy, and integrated lidar distributional metrics, respectively). It is clear that the correction of intensity values is an essential step for the estimation of forest volume.     

Robust optimization using a gradient index: MEMS applications

This paper discusses a simple and effective robust optimization formulation and illustrates its application to MicroElectroMechanical Systems (MEMS) devices. The proposed formulation improves robustness of the objective function by minimizing a gradient index (GI), defined as a function of gradients of performance functions with respect to uncertain variables. The level of constraint feasibility is also enhanced by adding a term determined by a constraint value and the gradient index. In the robust optimal design procedure, a deterministic optimization for performance improvement is followed by a sensitivity analysis with respect to uncertainties such as MEMS fabrication errors and changes of material properties. During the process of the deterministic optimization and sensitivity analysis, dominant performances and critical uncertain variables are identified to define the GI. Our approach for robust design requires no statistical information on the uncertainties and yet achieves robustness effectively. Two MEMS application examples including a micro accelerometer and a resonant-type micro probe are presented.     

A Calculus of Virtual Community Knowledge Intentions: Anonymity and Perceived Network-Structure

This study investigates the underlying motivational factors of knowledge exchange intentions (intention to obtain and to provide knowledge) within virtual community contexts. Perceived virtual network structures, namely virtual network connectivity (CN) and virtual network closeness (CL), are suggested as the important antecedents of knowledge sharing intentions in the context of virtual knowledge exchange communities. Anonymity (AN), one of the unique characteristics of virtual communities, but controversial due to its multi-faceted effects, is considered in a structural model as a factor having an impact on a virtual network structure. Data collected from participants of virtual communities through online surveys are analyzed using Partial Least Squares (PLS) structural equation modeling (SEM) to empirically test the proposed hypotheses. The results reveal that both CN and CL have a significant impact on both of the knowledge exchange intentions although CL shows an opposite direction of the impact. The results also show that AN has a significant impact on CL as expected but not on CN. Implications of this study may shed some light on better understanding community participants’ intentions to obtain and provide knowledge, along with the impact of anonymity on the perceived network structure.     

A class of adaptive model- and object-driven nonuniform sampling methods for 3-D inspection

Inspection of manufactured parts and assemblies often requires large amounts of information in the form of test probe point locations and large amounts of time to perform the inspection. By optimally locating the probe points it is possible to maintain inspection reliability using fewer test probes in a reduced amount of time. We have developed algorithms which use part model and manufacturing process information to generate an optimal probe-point location set for routine inspection in a modelbased, open-loop mode. An alternate set of adaptive algorithms that sequentially generates probe-point locations in an object-based, closed-loop mode characterizes a fault when one is detected. Test results show that the algorithms perform favorably on a large class of surfaces.     

Project Change Management System

Changes in projects are common and may be deleterious or beneficial—whether you see a change as a conflict or a valuable lesson depends only on your prospective. Project changes affect the cost, the scheduling, and the duration of projects, both directly and indirectly. Despite many articles and much discussion in practice and academic literature, there is a lack of information about systematic approaches to manage project change. This paper introduces a comprehensive project change management system that is founded on five principles: (1) promote a balanced change culture; (2) recognize change; (3) evaluate change; (4) implement change; and (5) continuously improve from lessons learned. By applying this project change management system, project participants can minimize deleterious change and promote beneficial change.     

Analyzing Enterprise Resource Planning System Implementation Success Factors in the Engineering–Construction Industry

Enterprise resource planning (ERP) systems offer many benefits to the engineering–construction industry. Many construction firms recognize the benefits of ERP system implementation; however, they still hesitate to adopt these systems due to high cost, uncertainties, and risks. This study identifies and analyzes critical factors that need to be considered to ensure successful ERP system implementation in the construction industry. First, this paper identifies the factors associated with the success and failure of ERP systems, and provides indicators to evaluate the success of such systems. Then, the paper develops an information system success model to analyze the relationships between factors and success indicators. Finally, the paper provides recommendations for successful ERP systems based on the analysis. The derived success factors should help senior managers in construction firms make better decisions and improve their business value by implementing the most effective EPR systems.     

Probing the reaction pathway of dehydrogenation of the LiNH2 + LiH mixture using in situ H NMR spectroscopy

Using variable temperature in situ ¹H NMR spectroscopy on a mixture of LiNH₂ + LiH that was mechanically activated using high-energy ball milling, the dehydrogenation of the LiNH₂ + LiH to Li₂NH + H₂ was investigated. The analysis indicates NH₃ release at a temperature as low as 30 °C and rapid reaction between NH₃ and LiH at 150 °C. The transition from NH₃ release to H₂ appearance accompanied by disappearance of NH₃ confirms unambiguously the two-step elementary reaction pathway proposed by other workers.