Ran Zhao

Zhen Liang, Taoran Wu, Ran Zhao et al.

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectiveness and efficiency. However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question. To address this, this paper introduces the UR4NNV verification framework, which utilizes under-approximation reachability analysis for DNN verification for the first time. UR4NNV focuses on DNNs with Rectified Linear Unit (ReLU) activations and employs a binary tree branch-based under-approximation algorithm. In each epoch, UR4NNV under-approximates a sub-polytope of the reachable set and verifies this polytope against the given property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN properties while providing confidence levels when reaching verification epoch bounds and failing falsifying properties. Experimental comparisons with existing verification methods demonstrate the effectiveness and efficiency of UR4NNV, significantly reducing the impact of the "unknown dilemma".

Jianwei Xu, Ran Zhao, Yizhou Yu et al.

Automatic detection of polyps is challenging because different polyps vary greatly, while the changes between polyps and their analogues are small. The state-of-the-art methods are based on convolutional neural networks (CNNs). However, they may fail due to lack of training data, resulting in high rates of missed detection and false positives (FPs). In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information. Firstly, we use a 2-D detector network to detect static images and frames, and based on the detector network, we propose two feature enhancement modules-the FP Relearning Module (FPRM) to make the detector network learning more about the features of FPs for higher precision, and the Image Style Transfer Module (ISTM) to enhance the features of polyps for sensitivity improvement. In video detection, we integrate spatiotemporal information, which uses Structural Similarity (SSIM) to measure the similarity between video frames. Finally, we propose the Inter-frame Similarity Correlation Unit (ISCU) to combine the results obtained by the detector network and frame similarity to make the final decision. We verify our method on both private databases and publicly available databases. Experimental results show that these modules and units provide a performance improvement compared with the baseline method. Comparison with the state-of-the-art methods shows that the proposed method outperforms the existing ones which can meet real-time constraints. It's demonstrated that our method provides a performance improvement in sensitivity, precision and specificity, and has great potential to be applied in clinical colonoscopy.

Ran Zhao, Hanchen Yao, Houde Dai

Magnetic response soft robot realizes programmable shape regulation with the help of magnetic field and produces various actions. The shape control of magnetic soft robot is based on the magnetic anisotropy caused by the orderly distribution of magnetic particles in the elastic matrix. In the previous technologies, magnetic programming is coupled with the manufacturing process, and the orientation of magnetic particles cannot be modified, which brings restrictions to the design and use of magnetic soft robot. This paper presents a magnetic pixel robot with shape programmable function. By encapsulating NdFeB/gallium composites into silicone shell, a thermo-magnetic response functional film with lattice structure are fabricated. Basing on thermal-assisted magnetization technique, we realized the discrete magnetization region distribution on the film. Therefore, we proposed a magnetic coding technique to realize the mathematical response action design of software robot. Using these methods, we prepared several magnetic soft robots based on origami structure. The experiments show that the behavior mode of robot can be flexibly and repeatedly regulated by magnetic encoding technique. This work provides a basis for the programmed shape regulation and motion design of soft robot.