张洪博

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教师拼音名称:zhanghongbo

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性别:男

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个人简介Personal Profile

工学博士,统计学博士后,硕士研究生导师,长期从事工业人工智能前沿研究,专注于机器视觉与工业大数据分析的理论创新及产业应用。发表SCI论文10篇(中科院分区13篇,TOP 4篇),主持或主要参与科研项目7项。

主讲本科生课程人工智能技术及其应用》、智能工厂规划设计、《计算机控制系统综合实验》,研究生课程《信息检索与论文写作指导》。


发表论文:

1.Twin Q-learning-driven forest ecosystem optimization for feature selection[J]. Knowledge-Based Systems, 2025, 315: 113323.(1区,TOP)

2. Reinforcement learning guided auto-select optimization algorithm for feature selection[J]. Expert Systems with Applications, 2025, 268: 126320.(1区,TOP)

3. Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation[J]. Applied soft computing, 2020, 90: 106157.(1区,TOP)

4. Improved salp swarm algorithm based on Newton interpolation and cosine opposition-based learning for feature selection[J]. Mathematics and Computers in Simulation, 2024, 219: 544-558.(2区,TOP)

5. Probe mechanism based particle swarm optimization for feature selection[J]. Cluster Computing, 2024, 27(6): 8393-8411.

6. Grasshopper optimization algorithm with principal component analysis for global optimization[J]. The Journal of Supercomputing, 2020, 76(7): 5609-5635. 

7. An improved bat algorithm and its application in multi-level image segmentation[J]. Journal of Intelligent & Fuzzy Systems, 2019, 37(1): 1399-1413.

8. A multi-level image thresholding approach using Otsu based on the improved invasive weed optimization algorithm[J]. Signal, Image and Video Processing, 2020, 14(3): 575-582. 

9. Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation[J]. Arabian Journal for Science and Engineering, 2019, 44(11): 9221-9234. 

10. A novel industrial image contrast enhancement technique based on an improved ant lion optimizer[J]. Arabian Journal for Science and Engineering, 2021, 46: 3235-3246.

主持或参与科研项目:

1.基于深度学习与协同粒子群算法的小样本表面缺陷识别方法研究,吉林省科技厅

2. 乱序堆叠零件机器人视觉拾取技术的研究与开发,吉林省发改委

3. 轿车冲压工业机器人视觉检测系统的研究,吉林省科技厅

4. 基于深度学习的滚动轴承故障诊断技术研究,吉林省教育厅

5. 基于数字图像相关的杜仲胶负重轮滚动力学特性研究,校企联合课题

6. 电动车发罩总成设计与开发,校企联合课题

7. 基于数字图像相关的汽车轮胎胎侧高速滚动力学特性研究,吉林省教育厅


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