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The purpose of this paper is to study the movement characteristics of micro drill bit during entry period in printed circuit board (PCB) high-speed drilling and to present an effective method to conduct quantitative analysis of the wandering of drill bit based on high-speed video capturing.

Based on the high-speed camera technology, experiments are conducted to get a series of time sequence images and the wandering of micro drill tip and the radial run-out of drill body, and the max-deformation of drill bit are calculated by using a quantitative analysis method. Finally, the movement characteristics of micro drill bit during entry drilling period PCB high-speed drilling are evaluated.

With the increasing spindle speed, the radial run-out of drill body decreases gradually, whereas the wandering amplitude of the drill point gradually increases; micro drill bit itself has an ability of positioning deviation correction after contacting the entry sheet; the feed rate within a certain range could slightly worsen the deformation of drill tip at the instant of impingement.

With the improvement of spindle speed, the camera’s shooting speed needed will increase accordingly, thus, the resolution of the pictures will decline, which always affects the analysis precision.

A series of effective methods to conduct quantitative analysis of the wandering micro drill bit by using high-speed camera technology is presented; a reference for the optimization of micro-hole drilling is provided.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

In the binary sex-segregated space of professional sports, sex-gender diversity is met with suspicion, derision and exclusion. In the United States, along with widespread anti-trans policies at various societal levels, legislations and regulations are being pushed to limit or eliminate transgender athletes from competing in all levels of sports. However, little scholarship has considered the implications of the presence of nonbinary athletes, those who identify outside the spectrum of man and woman, beyond the conversation of a ‘third gender’ category in sport. In this chapter, I seek to examine how nonbinary athletes embody disobedience by challenging the binary categorization of sex-gender within professional sports. I explore the racialized embodiment of sex and gender in professional women's sports, specifically WNBA player Layshia Clarendon. I explore how disobedience is employed to incite resistance against the narrow sex-gender categories that are forced upon athletes. Finally, I argue that embodied disobedience provides a key pathway for nonbinary athletes to undermine the regulatory nature of sex-gender categorization in sport. Particularly, nonbinary athletes may seek medical and social forms of gender affirmation, while simultaneously embodying disobedience by continuing to actively participate in professional sports categories in which they may not neatly fit.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

This study presents an image processing algorithm capable of calculating selected flight parameters requested by flight control systems to guide aircraft along the horizontal projection of the landing trajectory. The parameters identified based on the basics of the image of the Calvert light system appearing in the on-board video system are used by flight control algorithms that imitate the pilot’s schematics of control. Controls were generated using a fuzzy logic expert system. This study aims to analyse an alternative to classical solutions that can be applied to some specific cases.

The paper uses theoretical discussions and breakdowns to create the basics for the development of structures for both image processing algorithms and control algorithms. An analytical discussion on the first stage was transformed into laboratory rig tests using a real autopilot unit. The results of this research were verified in a series of software-in-the-loop computer simulations.

The image processing method extracts the most crucial parameters defining the relative position of the aircraft to the runway, as well as the control algorithm that uses it.

In flight control systems that do not use any dedicated ground or satellite infrastructure to land the aircraft.

This paper presents the original approach of the author to aircraft control in cases where visual signals are used to determine the flight trajectory of the aircraft.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

This paper aims to introduce a novel method for the analysis of open cell porous components fabricated by laser-based powder bed metal additive manufacturing (AM) for the purpose of quality control. This method uses photogrammetric analysis, the extraction of geometric information from an image through the use of algorithms. By applying this technique to porous AM components, a rapid, low-cost inspection of geometric properties such as material thickness and pore size is achieved. Such measurements take on greater importance, as the production of porous additive manufactured orthopaedic devices increases in number, causing other, slower and more expensive methods of analysis to become impractical.

Here the development of the photogrammetric method is discussed and compared to standard techniques including scanning electron microscopy, micro computed tomography scanning and the recently developed focus variation (FV) imaging. The system is also validated against test graticules and simple wire geometries of known size, prior to the more complex orthopaedic structures.

The photogrammetric method shows an ability to analyse the variability in build fidelity of AM porous structures for use in inspection purposes to compare component properties. While measured values for material thickness and pore size differed from those of other techniques, the new photogrammetric technique demonstrated a low deviation when repeating measurements, and was able to analyse components at a much faster rate and lower cost than the competing systems, with less requirement for specific expertise or training.

The advantages demonstrated by the image-based technique described indicate the system to be suitable for implementation as a means of in-line process control for quality and inspection applications, particularly for high-volume production where existing methods would be impractical.

This paper aims to present a digital image processing (DIP)-based discrete element method (DEM) for the analysis of heterogeneous geomaterials. Taking a soil and rock mixture as an example, the direct shear test is used to illustrate the application of this method. The numerical result is validated by the laboratory experiment and implies its feasibility in the analysis of heterogeneous geomaterials.

This method has two major steps. Based on a modification of the connected-component labeling algorithm, a novel vectorization method, which can transform the digital photos to vectorized geometry automatically, is proposed first. Then, a simple yet effective method for the generation of heterogeneous DEM models is presented using the simulation of simplicity technique.

DIP-DEM method is a feasible approach for the analysis of mechanical behavior of heterogeneous material. For soil and rock mixtures (SRM), the horizantal deformation at peak shear point becomes larger with the normal stress. Compared with pure soil, the rock aggregates mainly improve the friction angle of SRM.

As a universal method taking advantage of both DIP and DEM, this method has broad application prospects in related fields.

Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.

LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.

Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.

It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.

Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

Generally, Internet-of-Things (IoT) is quite small sized with limited resource and low cost that may be vulnerable for physical and cloned attacking. All kind of authentication protocols designed to IoT devices are robust despite which it is prone to attack by hackers. In order to resolve this issue, there are various researches that have introduced the best method for obscuring the cryptographic key. However, the studies have majorly aimed to generate the key dynamically from noise data by Fuzzy Extractor (FE) or Fuzzy Commitment (FC). Hence, these methods have utilized this kind of data with noisy source namely Physical Unclonable Function (PUF) or biometric data. There are several IoT devices that get operated over undermined environment in which biometric data is not available but the technique utilized with biometric data can't be used to undermined IoT devices. Even though, the PUF technique is implemented for the undermined IoT devices this is quite vulnerable over physical attacks inclusive of accidental move and theft.

This paper has proposed an advanced scheme in fuzzy commitment over IoT devices which is said to be Improved Two Factor Fuzzy Commitment Scheme (ITFFCS) and this proposed ITFFCS has used two kind of noisy factors present inside and outside the IoT devices. Though, an intruder has accomplished the IoT devices with an access to the internal noisy source, the intruder can't select an exact key from the available data which have been compared using comparable module as an interest.

Moreover, the proposed ITFFC method results are compared with existing Static Random Accessible Memory (SRAM) PUF in enterprises application which illustrated the proposed ITFFC method with PUF has accomplished better results in parameters such as energy consumption, area utilization, False Acceptance Ratio (FAR) and Failure Rejection Ratio (FRR).

Thus, the proposed ITFFCS-PUF is comparatively better than existing method in both FAR and FRR with an average of 0.18% and 0.28%.