Analysis of Technical Key Points in Road and Bridge Construction Management

With the rapid development of the country, the overall strength of the country is also increasing. With the rapid development of various industries in the country, more new technologies have been introduced and more new materials have been adopted in highway and bridge construction. At the same time, improving the quality of road and bridge construction and prolonging the service life of road and bridge engineering are also the primary tasks of construction enterprises. Therefore, in order to improve the quality of road and bridge engineering and extend its service life, it is necessary to improve the quality of road and bridge engineering on the premise of ensuring the construction focus. In the process of construction, the construction unit must effectively and reasonably choose the bridge construction technology, technological process and construction points that conform to itself, and only in this way can the quality of bridge engineering be guaranteed. In the process of road and bridge construction, the construction unit must comprehensively consider all aspects, carefully and conscientiously do every procedure, and take effective measures against various problems to ensure the construction quality, save costs and promote the development of road and bridge undertakings in China

Deep Convolutional Networks without Learning the Classifier Layer

Deep convolutional neural networks (CNNs) are effective and popularly used in a wide variety of computer vision tasks, especially in image classification. Conventionally, they consist of a series of convolutional and pooling layers followed by one or more fully connected (FC) layers to produce the final output in image classification tasks. This design descends from traditional image classification machine learning models which use engineered feature extractors followed by a classifier, before the widespread application of deep CNNs. While this has been successful, in models trained for classifying datasets with a large number of categories, the fully connected layers often account for a large percentage of the network\u27s parameters. For applications with memory constraints, such as mobile devices and embedded platforms, this is not ideal. Recently, a family of architectures that involve replacing the learned fully connected output layer with a fixed layer has been proposed as a way to achieve better efficiency. This research examines this idea, extends it further and demonstrates that fixed classifiers offer no additional benefit compared to simply removing the output layer along with its parameters. It also reveals that the typical approach of having a fully connected final output layer is inefficient in terms of parameter count. This work shows that it is possible to remove the entire fully connected layers thus reducing the model size up to 75% in some scenarios, while only making a small sacrifice in terms of model classification accuracy. In most cases, this method can achieve comparable performance to a traditionally learned fully connected classification output layer on the ImageNet-1K, CIFAR-100, Stanford Cars-196, and Oxford Flowers-102 datasets, while not having a fully connected output layer at all. In addition to comparable performance, the method featured in this research also provides feature visualization of deep CNNs at no additional cost

Discussion on Methods and Measures of Road and Bridge Construction Project Management

In the process of domestic economic construction, various road and bridge engineering projects have started construction, which is of great significance for the development of the domestic economy and is also the key to the national economy and people's livelihood. Therefore, it is necessary to pay attention to the construction and management of bridge projects. This article discusses the significance and importance of project management in road and bridge engineering construction, and explores its project management strategies. In recent years, the construction scope of road and bridge projects in many regions of China has expanded, putting forward more and higher requirements for road and bridge project management. This article explores the management methods and strategies for road and bridge construction projects, aiming to enhance project management efforts and strengthen project construction quality. This article combines its own construction experience and determines the importance of road and bridge project construction management based on reference method and case analysis method. From construction practice, it can be concluded that in the new era of road and bridge construction, it is necessary to focus on implementing the project manager responsibility system and strengthening the construction of project management teams. Properly manage materials and labor, reasonably apply mechanical equipment, formulate a comprehensive personnel reward and punishment system, summarize construction management experience, improve various project management systems, select targeted management methods, and improve the effectiveness of project construction

Facial Motion Prior Networks for Facial Expression Recognition

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.Comment: VCIP 2019, Oral. Code is available at https://github.com/donydchen/FMPN-FE

Analysis of Stage-discharge Relationship with Neural Network in Varying Backwater Zone of Three Gorges Project

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

This study surveyed 253 truck drivers and found that many drivers scored poorly on the Stanford and Epworth sleepiness scales indicating that they may not be as alert as they should be while driving. Moreover, those who rated the air in their truck cabins as fresh reported less irritation to their eyes, noses, throats, and skin, scored better in both sleepiness scales, and reported fewer sleep-related medical symptoms. Finally, the results of the ordinal logistic model indicate that drivers' perceptions of the air quality in their truck cabins are significantly related to their alertness during a trip

Relationship Between Perceived In-Cabin Air Quality and Truck Drivers' Self-Reported Health and Alertness

This study surveyed 253 truck drivers and found that many drivers scored poorly on the Stanford and Epworth sleepiness scales indicating that they may not be as alert as they should be while driving. Moreover, those who rated the air in their truck cabins as fresh reported less irritation to their eyes, noses, throats, and skin, scored better in both sleepiness scales, and reported fewer sleep-related medical symptoms. Finally, the results of the ordinal logistic model indicate that drivers' perceptions of the air quality in their truck cabins are significantly related to their alertness during a trip

Mining the Relation between Sentiment Expression and Target Using Dependency of Words

PACLIC 20 / Wuhan, China / 1-3 November, 200

Probability density forecasts for steam coal prices in China:The role of high-frequency factors

Abstract Coal plays a key role in China's economy as a dominant primary energy resource. In this paper, we provide probability density forecasts for weekly steam coal prices in China based on daily factors such as renewable energy source, Daqing oil, Japanese natural gas, Australia steam coal prices, coal mining industry index, A-share power sector index, A-share index, coal industry index, and temperature. The empirical results show that the influence of temperature lasts longer than other factors, while the Australia steam coal prices, renewable energy source and A-share index are the three best predictors for steam coal prices. It is also shown that the high-frequency factors are useful to forecast steam coal prices and that considering the nonlinearity of coal prices can improve the forecast accuracy by about 22%. We further provide the probability density forecasts for steam coal prices based on the influence of all the selected factors, the results suggest that our proposed method can provide accurate and satisfying probability density forecasts. Given these results, the policy-makers can make effective strategies which can not only adjust the energy structure but also ensure economic growth