Consultant Software Quality Engineer

本文介绍了一个测试工程师如何运用技能、能力和经验推动行业竞争产品的市场发展。通过与测试团队、项目管理和开发部门的有效互动,测试工程师能够影响测试方向,并指导经验较少的工程师。文章还强调了在多个产品领域成为主题专家的重要性,以及在自动化设计和使用方面的经验。

General Summary

  • Uses skills, abilities and experiences to drive industry competitive products to market through skillful interactions with test team, project management, development and management. Influences test direction and mentors less experienced engineers around them.
  • Subject matter expert for multiple product areas. Experienced with design and use of automation, capable of learning subsystem internals, working with developers and establishing coverage targets including prioritization for test case automation.

Responsibilities

  • Works on complex problems and provides solutions.
  • Determines test requirements through working with developers and interfacing with project management.
  • Provides test focused feedback to development in order to influence product design to enhance testability.
  • Actively seeks input, and takes responsibility to innovate and solve problems.
  • Uses independent judgment to accomplish goals and objectives.
  • Acts as prime consultant on critical projects that impact long term organizational goals and objectives.

Requirements

Problem solving skills.

  • General knowledge and application of engineering and test concepts.
  • Thorough understanding of automation environments.
  • Experience with Agile test methodologies
  • Ability to lead, motivate and direct a workgroup.
  • Communication skills.
  • Mentoring/Coaching skills.
  • Presentation skills.

内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等多种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高校研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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