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| 1 | TDD-net: a tiny defect detection network for printed circuit boards显示文摘Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available. | Runwei Ding Linhui Dai Guangpeng Li Hong Liu | 2019 | CAAI Transactions on Intelligence Technology2019，4，2： | 27 |
| 2 | ACP-based social computing and parallel intelligence: Societies 5.0 and beyond显示文摘 | XiaoWang Lingxi Li Yong Yuan Peijun Ye Fei-Yue Wang | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 21 |
| 3 | Multi-robot path planning based on a deep reinforcement learning DQN algorithm显示文摘The unmanned warehouse dispatching system of the‘goods to people’model uses a structure mainly based on a handling robot,which saves considerable manpower and improves the efficiency of the warehouse picking operation.However,the optimal performance of the scheduling system algorithm has high requirements.This study uses a deep Q-network(DQN)algorithm in a deep reinforcement learning algorithm,which combines the Q-learning algorithm,an empirical playback mechanism,and the volume-based technology of productive neural networks to generate target Q-values to solve the problem of multi-robot path planning.The aim of the Q-learning algorithm in deep reinforcement learning is to address two shortcomings of the robot path-planning problem:slow convergence and excessive randomness.Preceding the start of the algorithmic process,prior knowledge and prior rules are used to improve the DQN algorithm.Simulation results show that the improved DQN algorithm converges faster than the classic deep reinforcement learning algorithm and can more quickly learn the solutions to path-planning problems.This improves the efficiency of multi-robot path planning. | Yang Yang Li Juntao Peng Lingling | 2020 | CAAI Transactions on Intelligence Technology2020，5，3： | 15 |
| 4 | A study on key technologies of unmanned driving显示文摘 | Xinyu Zhang Hongbo Gao Mu Guo Guopeng Li Yuchao Liu Deyi Li | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 14 |
| 5 | A survey on rough set theory and its applications显示文摘 | Qinghua Zhang Qin Xie Guoyin Wang | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 13 |
| 6 | Social network search based on semantic analysis and learning显示文摘 | Feifei Kou Junping Du Yijiang He Lingfei Ye | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 11 |
| 7 | Image-denoising algorithm based on improved K-singular value decomposition and atom optimization显示文摘The traditional K-singular value decomposition(K-SVD)algorithm has poor imagedenoising performance under strong noise.An image-denoising algorithm is proposed based on improved K-SVD and dictionary atom optimization.First,a correlation coefficient-matching criterion is used to obtain a sparser representation of the image dictionary.The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary.Then,non-local regularity is incorporated into the denoising model to further improve image-denoising performance.Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge. | Rui Chen Dong Pu Ying Tong Minghu Wu | 2022 | CAAI Transactions on Intelligence Technology2022，7，1： | 5 |
| 8 | Online RGB-D person re-identification based on metric model update显示文摘 | Hong Liu Liang Hu Liclian Ma | 2017 | CAAI Transactions on Intelligence Technology2017，2，1： | 5 |
| 9 | Background modeling methods in video analysis: A review and comparative evaluation显示文摘 | Yong Xu Jixiang Dong Bob Zhang Daoyun Xu | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 4 |
| 10 | Recent Advances on Human-Computer Dialogue显示文摘 | Xiaojie Wang Caixia Yuan | 2016 | CAAI Transactions on Intelligence Technology2016，1，4： | 4 |
| 11 | Multi-LeapMotion sensor based demonstration for robotic refine tabletop object manipulation task显示文摘 | Haiyang Jin Qing Chen Zhixian Chen Ying Hu Jianwei Zhang | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 4 |
| 12 | Efficient key management scheme for health blockchain显示文摘 | Huawei Zhao Peidong Bai Yun Peng Ruzhi Xu | 2018 | CAAI Transactions on Intelligence Technology2018，3，2： | 3 |
| 13 | Survey on cloud model based similarity measure of uncertain concepts显示文摘It is a basic task to measure the similarity between two uncertain concepts in many real-life artificial intelligence applications,such as image retrieval,collaborative filtering,public opinion guidance,and so on.As an important cognitive computing model,cloud model has been used in many fields of artificial intelligence.It can realise the bidirectional cognitive transformation between qualitative concept and quantitative data based on the theory of probability and fuzzy set.The similarity measure of two uncertain concepts is a fundamental issue in cloud model theory.Popular similarity measure methods of cloud model are surveyed in this study.Their limitations are analysed in detail.Some related future research topics are proposed. | Shuai Li Guoyin Wang Jie Yang | 2019 | CAAI Transactions on Intelligence Technology2019，4，4： | 3 |
| 14 | A framework for multi-session RGBD SLAM in low dynamic workspace environment显示文摘 | Yue Wang Shoudong Huang Rong Xiong Jun Wu | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 3 |
| 15 | Sensor-based complete coverage path planning in dynamic environment for cleaning robot显示文摘 | Hong Liu Jiayao Ma Weibo Huang | 2018 | CAAI Transactions on Intelligence Technology2018，3，1： | 3 |
| 16 | Relative attribute based incremental learning for image recognition显示文摘 | Emrah Ergul | 2017 | CAAI Transactions on Intelligence Technology2017，2，1： | 3 |
| 17 | Face recognition using both visible light image and near-infrared image and a deep network显示文摘 | Kai Guo Shuai Wu Yong Xu | 2017 | CAAI Transactions on Intelligence Technology2017，2，1： | 3 |
| 18 | Efficient algorithm for big data clustering on single machine显示文摘Big data analysis requires the presence of large computing powers, which is not always feasible. And so, itbecame necessary to develop new clustering algorithms capable of such data processing. This study proposes a newparallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth ofcomputations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initialdataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for eachbatch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real largedatasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposedapproach is compared with k-means and its modification. The experiments show that the proposed algorithm is apromising tool for clustering large datasets in comparison with the k-means algorithm. | Rasim M.Alguliyev Ramiz M.Aliguliyev Lyudmila V.Sukhostat | 2020 | CAAI Transactions on Intelligence Technology2020，5，1： | 3 |
| 19 | Deep learning approach for microarray cancer data classification显示文摘Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality,smaller sample size,imbalanced number of classes,noisy data-structure,and higher variance of feature values.This has led to lesser classification accuracy and over-fitting problem.In this work,the authors aimed to develop a deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes.They have used a 7-layer deep neural network architecture having various parameters for each dataset.The small sample size and dimensionality problems are addressed by considering a well-known dimensionality reduction technique namely principal component analysis.The feature values are scaled using the Min-Max approach and the proposed approach is validated on eight standard microarray cancer datasets.To measure the loss,a binary cross-entropy is used and adaptive moment estimation is considered for optimisation.The performance of the proposed approach is evaluated using classification accuracy,precision,recall,f-measure,log-loss,receiver operating characteristic curve,and confusion matrix.A comparative analysis with state-of-the-art methods is carried out and the performance of the proposed approach exhibit better performance than many of the existing methods. | Hema Shekar Basavegowda Guesh Dagnew | 2020 | CAAI Transactions on Intelligence Technology2020，5，1： | 3 |
| 20 | Safety control strategy for vertebral lamina milling task显示文摘 | Luping Fan Peng Gao Baoliang Zhao Yu Sun Xiaoxiao Xin Ying Hu Shoubin Liu Jianwei Zhang | 2016 | CAAI Transactions on Intelligence Technology2016，1，3： | 3 |
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