科技论文正文的基本结构，包括引言、研究方法、实验/研究设计、结果和讨论，最后以结论结尾。在这一系列讲座中，我们将对各个部分分别加以介绍。这里，首先介绍引言的撰写。
英文科技论文的引言中所要说明的问题有以下几个方面：1) 所要解决的研究问题 (What)，2) 研究的动机(Why)，3) 研究的理论基础和研究方法(How)，4) 研究过程中所要完成的主要任务(Tasks Completed)，5) 研究所获得的结果(Results Obtained)，6) 研究的创新性(Contributions/Innovations made)，7) 研究的结论(Conclusions Drawn)。这些问题的回答将为读者提供对整个研究项目的清晰了解，并为后续论文的发展打下基础。
引言(Introduction)在科技论文中的作用是为整个研究设立基调，向读者提供足够的信息，使其能够理解研究的上下文、动机和目标。一个好的引言能够为读者创造一个理解和欣赏研究价值的框架，从而为论文研究内容提供良好的背景和理解基础。本节主要介绍：
引言的作用
引言中的主要内容
引言撰写所需要注意的问题
科技论文中题目、摘要和引言之间的关系
引言的示例
引言的作用
英文科技论文中的引言是整篇文章的开篇部分，其作用非常重要。引言的作用主要包括以下几个方面：
引导读者： 引言的首要任务是引导读者进入研究的主题。它通过提供背景信息、概述研究领域的现状，以及引入具体的研究问题，使读者对整个研究有一个清晰的认识。
提供背景信息： 引言向读者介绍了研究领域的基本概念、关键术语和先前研究的现状。这有助于确保读者对研究的背景有足够的了解，从而更好地理解后续的论文内容。
明确研究问题： 引言阐明了研究中要解决的问题或要回答的科学/技术疑问。这有助于读者理解研究的动机和目标，以及为什么这个研究是重要的。
论证研究的重要性： 引言通过阐述研究的重要性和潜在影响，向读者传达研究的价值。这可以包括解决实际问题、推动学科进步、促使技术创新等方面。
设立研究框架： 引言通常概述了研究的基本框架，包括研究的目标、方法论、预期结果等。这有助于读者了解研究的整体结构和走向。
激发兴趣： 引言应当具有引人入胜的特点，激发读者的兴趣，使其愿意继续阅读后续内容。清晰、有趣的引言可以在读者心中留下积极的印象。
引言中的主要内容
撰写英文科技论文引言时，主要内容通常包括以下方面，每一方面都有其独特的目标和组成部分：
引入背景和上下文：
目标：引起读者兴趣，为研究提供合适的语境。
组成部分：介绍研究领域的整体背景，突出当前问题或挑战。
示例：In the era of rapid technological advancement, the field of cybersecurity plays a pivotal role in safeguarding sensitive information against evolving threats.
问题陈述和研究动机：
目标：明确研究的核心问题，解释为什么这个问题值得关注。
组成部分：清晰地陈述研究的问题或目标，并强调其重要性。
示例：Despite continuous efforts to enhance cybersecurity measures, the increasing sophistication of cyber attacks necessitates novel approaches to ensure the integrity and confidentiality of digital information.
文献综述：
目标：提供关于已有研究的背景知识，揭示研究的位置和创新点。
组成部分：简要回顾相关文献，强调先前工作的关键发现和尚未解决的问题。
示例：Prior research has extensively explored cryptographic methods for data encryption, yet there remains a gap in understanding the vulnerabilities introduced by quantum computing to these established techniques.
研究目标或假设：
目标：明确研究的目标或提出研究的基本假设。
组成部分：清晰地陈述研究的目标，或者如果有的话，提出研究的基本假设。
示例：This study aims to investigate the feasibility of integrating quantum-resistant cryptographic algorithms to enhance the security of digital communication in the era of quantum computing.
研究方法概述：
目标：提供关于研究设计和方法的简要概述。
组成部分：概括性地介绍研究采用的方法和实验设计。
示例：To achieve our research objectives, we employed a combination of theoretical analysis and simulation-based experiments using state-of-the-art quantum computers.
预期贡献：
目标：强调研究的创新性和预期的贡献。
组成部分：明确指出研究预计的具体贡献，可能是填补知识空白、提供新的解决方案或拓展理论框架。
示例：This research contributes to the field by shedding light on the potential vulnerabilities of current cryptographic methods in the era of quantum computing and proposing strategies for developing quantum-resistant cryptographic algorithms.
引导论文结构：
目标：提供一个简要的概述，介绍论文的其他部分中讨论的主要内容。
示例：This paper is structured as follows: Section 2 provides a detailed review of the current state of solar cell technology, Section 3 outlines our experimental methodology, and Section 4 presents the results and analysis.
综合这些内容，构建引言，引导读者深入论文，理解研究的背景、问题、目标以及方法。引言应该具有清晰的逻辑结构，以确保读者能够理解研究的重要性和独特性。
撰写英文科技论文引言时需要注意的问题
在撰写英文科技论文引言时，有几个关键问题需要特别注意：
清晰度和简洁性(Clarity and Conciseness)： 引言应该表达清晰、简洁，避免冗长和复杂的句子。确保用简单而明确的语言向读者传达研究的主题、背景和问题。
避免废话(Avoid Redundancy)： 不要在引言中重复大量信息，避免废话和不必要的描述。每句话都应该有明确的目的，有助于引导读者进入研究的主题。
专业术语的适度使用(Appropriate Use of Technical Terminology)： 如果领域内有专业术语，确保适当使用。但也要确保不使用过多的专业术语，以便非专业读者也能理解引言。
强调研究的重要性(Emphasize the Significance of the Study)： 突出研究的重要性，解释为什么读者应该关心这个研究，以及研究的潜在影响。
精确的问题陈述(Precise Problem Statement)： 确保问题陈述清晰、精确。避免使用模糊或含糊不清的措辞，而是明确定义研究中要解决的问题。
逻辑顺序(Logical Organization)： 引言应该按照逻辑顺序组织，确保各部分之间有清晰的过渡。读者应该能够理解为什么这个研究是有必要进行的。
引言末尾的预览(Preview at the End of the Introduction)： 在引言的末尾，可以提供一个简短的预览，概述接下来各个章节的内容。这有助于读者理解整个论文的结构。
文献引用(Literature Review)： 如果合适，引言中可以包含对相关文献的引用，以支持论点并为读者提供更多的背景信息。确保引用的文献是与研究直接相关的。
审慎使用引语和概括(Cautious Use of Quotes and Generalizations)： 避免过度使用引语，而是选择精心选取的引语，以及审慎使用概括性语句。
反复修改和修订(Iterative Editing and Revising)： 引言通常需要多次修改和修订。定期返回引言，确保其与论文的其他部分相协调，并且在读者的反馈下进行改进。
关注这些要点，可以确保引言在传达清晰信息、引导读者、强调研究的重要性等方面都能达到高质量的标准。
科技论文中题目、摘要和引言之间的关系
科技论文中，题目、摘要和引言是三个关键部分，它们之间存在密切的关系，各自承担不同的功能：
题目 (Title):
题目是论文内容的简明概括，是读者首次接触到的信息。题目应该准确、明确地反映论文的主题，引导读者对论文内容产生兴趣。
摘要 (Abstract):
摘要是对整篇论文的简短概述，通常在题目之后，引言之前出现。摘要包含论文的主要目标、方法、结果和结论，是读者在决定是否阅读整篇论文时的参考依据。
引言 (Introduction):
引言是论文的开篇，介绍研究问题、动机、理论基础、研究方法和预期的结果。引言提供了对论文整体结构和内容的全面了解，为后续部分的发展提供背景和理论基础。
三者之间关系：
题目与摘要：题目和摘要之间存在直接的关联，题目应该反映在摘要中提到的主要内容，以确保读者在阅读摘要时能够理解论文的核心信息。
摘要与引言：摘要提供了对整篇论文的高层次概述，引言则展开这一概述，详细介绍研究的背景、问题、动机等，使读者对研究有更深入的理解。
题目与引言：题目是引言的前奏，应当通过简洁而有力地表达论文的主题，为引言部分的展开提供引导。
论文中题目、摘要和引言之间是一种逐渐延伸扩展的关系，这有助于引导读者从整体到细节地理解论文。读者通过题目获得论文的整体主题，摘要提供了更详细的概述，引言则进一步展开并提供了深入的背景信息。这种结构使得读者能够渐进式地了解论文的内容，确保他们对研究的全貌有清晰的认识。这三个部分相互联系，构成论文整体结构，确保读者能够迅速了解研究的主题、目的和关键内容。
引言的示例
示例1: 控制理论领域
Title: Adaptive sliding mode observer for non-linear stochastic systems with uncertainties
Abstract: It is presented, in this paper, a novel adaptive sliding mode observer (ASMO) for reconstructing the states of non-linear stochastic systems with structure uncertainties, parameter perturbations and external disturbances which is presented in the Itô differential equations. The proposed ASMO uses sliding mode technique to guarantee the robustness of observation, and an adaptive law is employed to update the sliding mode gain. The estimation error of the proposed observer is theoretically proved to be mean square exponentially convergent to a limited bound. Simulation study is made on computer with MatLab for reconstructing the states of Lorenz chaotic attractor disturbed with uncertainties and polluted with noises, and the simulation results verify the effectiveness of the proposed observation strategy.
Introduction
The development and application of system state observers have attracted great attention since the original work of Luenberger (1966); it has been proven useful in system monitoring, regulation as well as detection and identification of system failures (Hu, 1991; Gauthier and Kupka, 1994; Bernard et al., 1998). The idea of using observers, also called software sensors, which combine a number of readily available online measurements with a process model for estimating or reconstructing the values of unmeasured state variables. Many state observation algorithms have been well developed for linear systems and much contribution has been made of state observation for non-linear dynamic systems using techniques like feedback linearisation, extended linearisation and Lyapunov-based algorithms (Bestle and Zeitz, 1983; Krener and Respondek, 1985; Isidori, 1985; Baumann and Rugh, 1986; Thau, 1973; Vidyasagar, 1980).
Variable structure technique (Utkin, 1977; Hung et al., 1993) has been a major tool in tackling the monitoring and control problem of non-linear dynamic systems with structure uncertainties, parameter perturbations and external disturbances since it emerged in about mid 20th century. And sliding mode observers have been successfully developed and employed as robust technique for state observation or estimation for this kind of systems (Slotine et al., 1987; Zhan et al., 1999).
However, there still remains a space to be filled with systematic strategies to deal with state reconstruction for non-linear stochastic systems. There are only few papers published concerning this area (Yaz and Azemi, 1993; Xu et al., 2004; Raoufi and Khaloozadeh, 2005; Qiao et al., 2008).
In this paper, a novel sliding mode observer based on adaptive strategy is investigated for non-linear stochastic systems with system structure uncertainties, excessive parameter perturbations and external disturbances, the estimation error with the proposed observer is theoretically proved to be mean square exponentially convergent to a limited region, which means that the system states reconstructed are within bounded error. A simulation study is made on computer with MatLab for state estimation of Lorenz chaotic attractor which is disturbed with excessive uncertainty and polluted with noises, and the numerical simulation results show the effectiveness of the proposed observation strategy.
The remaining part of this paper is organised as follows: in Section 2, some preliminaries for non-linear stochastic systems are stated and the problem is formulated, together with some definitions and assumptions defined and declared for easiness of discussion; in Section 3, a sliding mode observer is proposed based on adaptive scheme; and convergence of the proposed observer is investigated in Section 4 in the sense of Lyapunov theorem with Itô calculus; a simulation study is made in Section 5 to show the effectiveness of the proposed adaptive observer; and the paper is concluded in Section 6.
示例2: 人工智能领域
Introduction:
In the last decade, Artificial Intelligence (AI) has witnessed unprecedented growth, permeating diverse sectors and redefining our interactions with technology. The advent of deep learning, particularly convolutional neural networks (CNNs), has propelled AI's capabilities in image recognition tasks. However, despite these advancements, robustness in the face of adversarial attacks remains a critical challenge. This paper delves into the vulnerability of deep learning models to adversarial attacks and proposes a novel approach to enhance their resilience.
Background:
The surge in AI applications, from autonomous vehicles to medical diagnostics, underscores the transformative potential of deep learning models. Yet, the susceptibility of these models to subtle, carefully crafted adversarial inputs raises concerns about their reliability and security. Adversarial attacks, wherein imperceptible perturbations are introduced to input data, can lead to misclassifications with potentially severe consequences.
Problem Statement:
The vulnerability of deep learning models to adversarial attacks poses a substantial threat to their real-world deployment. Ensuring the robustness of these models is crucial, especially in safety-critical applications. This research addresses the pressing issue of adversarial vulnerability, aiming to develop techniques that fortify deep learning models against adversarial manipulations.
Significance of the Study:
By enhancing the robustness of deep learning models, this research contributes to the broader goal of deploying AI systems in safety-critical applications. The outcomes of this study have implications for sectors relying on AI, offering a more secure foundation for the integration of these technologies into real-world scenarios.
Research Objectives and Hypotheses:
Objectives:
To analyze the vulnerability of deep learning models to adversarial attacks.
To propose a novel framework for enhancing the robustness of deep learning models.
To evaluate the effectiveness of the proposed approach across diverse datasets and model architectures.
Hypotheses:
Adversarial vulnerability arises due to the non-robust features learned by deep learning models.
Incorporating adversarial training during model training enhances the robustness of deep learning models.
Methodology Overview:
The research employs a comprehensive methodology, involving the generation of adversarial examples through iterative optimization techniques. Adversarial training is incorporated during the model training phase to fortify the learned representations. The evaluation includes benchmark datasets and a range of deep learning architectures.
Expected Results:
It is anticipated that the proposed approach will demonstrate improved robustness against adversarial attacks, reflected in reduced misclassifications and improved generalization across datasets. The study aims to provide empirical evidence supporting the efficacy of adversarial training for enhancing model resilience.
Structure Overview:
The remainder of this paper is organized as follows: Section 2 reviews related work in adversarial attacks and defenses. Section 3 details the proposed methodology, including the generation of adversarial examples and adversarial training. Section 4 presents experimental results and analysis. Finally, Section 5 concludes the paper and outlines potential avenues for future research.
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