Seepage Mitigation in Hydropower Dams by Optimization in Roller Compacted Concrete Interlayer (Monoliths) Joint Bonding Technology

Roller Compacted Concrete (RCC) has gained favorable recognition in hydropower and water resource dam construction. With optimization in construction technology and materials used for RCC Dams, cost is no longer a major disadvantage as compared to environmental impact, that is, wildlife habitat disruption. In as much as it has become optimal for investment in hydropower dam construction, the scourge for dam failure is still eminent, which is as a result of excessive seepage compromising the integrity of the mechanical properties of the dam. The aim of the paper is to highlight successful application methods in joint bonding to avoid excessive seepage and reduce the autogenous healing to a few years of operation. In view of optimization, this paper presents a comprehensive study on the influences of interlayer joints bonding quality from RCC mix performances and how it consolidates the RCC layers to withstand the shear strength along the interface, especially on the high dams. The case study is the RCC dam at the 750 MW Kafue Gorge Lower Hydropower Station. The scope of the study reviews the joint type judged by Modified Maturity Factor (MMF) with joint surface long time exposed in regions with dry and high temperature, technical measures of layer bonding quality control under condition of long time joint surface exposure, effects of joints shear strength and impermeability of the RCC layers when under the conditions of plastic and elasticity. The subtle observations made during the dam construction phases were with respect to the optimal use of materials in relation to RCC mix designs and the basis for equipment calibration for monitoring important data that can be referenced during analysis of shear forces acting on the RCC dam over time.

How to Interpret Machine Knowledge

Machine knowledge refers to the knowledge contained in artificial intelligence.This article discusses how to acquire machine knowledge,with a particular focus on the acquisition of causal knowledge.The latter is the process of interpreting machine knowledge.Through the analysis of certain research methods in the fields of physics and artificial intelligence,we propose principles and models for interpreting machine knowledge,and discuss specific methods including the automation of the interpretation process and local linearization.

企业财务会计信息质量的影响因素与优化分析

企业内部财务会计信息一方面能够将企业实际运行情况反映出来,另一方面能够为企业发展指明方向。另外,财务会计信息是会计信息使用人员进行经济、战略决策的重要凭据。基于此,本文概述了企业财务会计整体信息质量现状,以此为基础,分析对企业财务会计整体信息质量产生影响的因素,并提出了优化企业财务会计整体信息质量的有效策略,以供参考。

LETRNG—A Lightweight and Efficient True Random Number Generator for GNU/Linux Systems

Unpredictable and irreproducible digital keys are required to modulate security-related information in secure communication systems.True random number generators(TRNGs)rather than pseudorandom number generators(PRNGs)are required for the highest level of security.TRNG is a significant component in the digital security realm for extracting unpredictable binary bitstreams.Presently,most TRNGs extract high-quality“noise”from unpredictable physical random phenomena.Thus,these applications must be equipped with external hardware for collecting entropy and converting them into a random digital sequence.This study introduces a lightweight and efficient true random number generator(LETRNG)that uses the inherent randomness of a central processing unit(CPU)and an operating system(OS)as the source of entropy.We then utilize a lightweight post-processing method based on XOR and fair coin operation to generate an unbiased random binary sequence.Evaluations based on two famous test suites(NIST and ENT)show that LETRNG is perfectly capable of generating high-quality random numbers suitable for various GNU/Linux systems.

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Sentence Boundary Disambiguation(SBD)is a preprocessing step for natural language processing.Segmenting text into sentences is essential for Deep Learning(DL)and pretraining language models.Tibetan punctuation marks may involve ambiguity about the sentences’beginnings and endings.Hence,the ambiguous punctuation marks must be distinguished,and the sentence structure must be correctly encoded in language models.This study proposed a component-level Tibetan SBD approach based on the DL model.The models can reduce the error amplification caused by word segmentation and part-of-speech tagging.Although most SBD methods have only considered text on the left side of punctuation marks,this study considers the text on both sides.In this study,465669 Tibetan sentences are adopted,and a Bidirectional Long Short-Term Memory(Bi-LSTM)model is used to perform SBD.The experimental results show that the F1-score of the Bi-LSTM model reached 96%,the most efficient among the six models.Experiments are performed on low-resource languages such as Turkish and Romanian,and high-resource languages such as English and German,to verify the models’generalization.

Machine Knowledge and Human Cognition

Intelligent machines are knowledge systems with unique knowledge structure and function.In this paper,we discuss issues including the characteristics and forms of machine knowledge,the relationship between knowledge and human cognition,and the approach to acquire machine knowledge.These issues are of great significance to the development of artificial intelligence.

Analysis on the development status of intelligent and connected vehicle test site

With the development of automobile intelligence and connectivity,Intelligent and Connected Vehicle(ICV)is an inevitable trend in the transformation and upgrading of the automotive industry.The maturity of any advanced technology is inseparable from a large number of test verifications,especially the research and application of automotive technology require a large number of reliable tests for evaluation and confirmation.Therefore,the ICV Test Site(ICVTS)will become a key deployment area.In this paper,we analyze the development status of ICVTS outside and within China,summarize the shortcomings of the existing test sites,and put forward some targeted suggestions,in an effort to guide the development and construction of ICVTS towards the path that seems to be most promising.

A Deep Learning Method for Chinese Singer Identification

As a subfield of Multimedia Information Retrieval(MIR), Singer IDentification(SID) is still in the research phase. On one hand, SID cannot easily achieve high accuracy because the singing voice is difficult to model and always disturbed by the background instrumental music. On the other hand, the performance of conventional machine learning methods is limited by the scale of the training dataset. This study proposes a new deep learning approach based on Long Short-Term Memory(LSTM) and Mel-Frequency Cepstral Coefficient(MFCC) features to identify the singer of a song in large datasets. The results of this study indicate that LSTM can be used to build a representation of the relationships between different MFCC frames. The experimental results show that the proposed method achieves better accuracy for Chinese SID in the MIR-1 K dataset than the traditional approaches.

PointGAT: Graph attention networks for 3D object detection

3D object detection is a critical technology in many applications,and among the various detection methods,pointcloud-based methods have been the most popular research topic in recent years.Since Graph Neural Network(GNN)is considered to be effective in dealing with pointclouds,in this work,we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT.Our proposed PointGAT outperforms previous approaches on the KITTI test dataset.Experiments in real campus scenarios also demonstrate the potential of our method for further applications.