22 research outputs found
Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation
An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA
Adversarial Examples in the Physical World: A Survey
Deep neural networks (DNNs) have demonstrated high vulnerability to
adversarial examples. Besides the attacks in the digital world, the practical
implications of adversarial examples in the physical world present significant
challenges and safety concerns. However, current research on physical
adversarial examples (PAEs) lacks a comprehensive understanding of their unique
characteristics, leading to limited significance and understanding. In this
paper, we address this gap by thoroughly examining the characteristics of PAEs
within a practical workflow encompassing training, manufacturing, and
re-sampling processes. By analyzing the links between physical adversarial
attacks, we identify manufacturing and re-sampling as the primary sources of
distinct attributes and particularities in PAEs. Leveraging this knowledge, we
develop a comprehensive analysis and classification framework for PAEs based on
their specific characteristics, covering over 100 studies on physical-world
adversarial examples. Furthermore, we investigate defense strategies against
PAEs and identify open challenges and opportunities for future research. We aim
to provide a fresh, thorough, and systematic understanding of PAEs, thereby
promoting the development of robust adversarial learning and its application in
open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense
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Highly potent multivalent VHH antibodies against Chikungunya isolated from an alpaca naĂŻve phage display library
Background
Chikungunya virus (CHIKV) is a re-emerged mosquito-borne alphavirus that can cause musculoskeletal diseases, imposing a substantial threat to public health globally. High-affinity antibodies are need for diagnosis and treatment of CHIKV infections. As a potential diagnostic and therapeutic agent, the multivalent VHH antibodies is a promising tookit in nanomedicine. Here, we developed potent multivalent VHH antibodies from an alpaca naïve phage display library targeting the E2 glycoprotein of the CHIKV virus.
Results
In the present study, we generated 20 VHH antibodies using a naïve phage display library for binders to the CHIKV E2 glycoprotein. Of these, multivalent VHH antibodies Nb-2E8 and Nb-3C5 had specific high-affinity binding to E2 protein within the nanomolar range. The equilibrium dissociation constant (KD) was between 2.59–20.7 nM, which was 100-fold stronger than the monovalent antibodies’ affinity. Moreover, epitope mapping showed that Nb-2E8 and Nb-3C5 recognized different linear epitopes located on the E2 glycoprotein domain C and A, respectively. A facile protocol of sandwich ELISA was established using BiNb-2E8 as a capture antibody and HRP-conjugated BiNb-3C5 as a detection antibody. A good linear correlation was achieved between the OD450 value and the E2 protein concentration in the 5–1000 ng/mL range (r = 0.9864, P < 0.0001), indicating its potential for quantitative detection of the E2 protein.
Conclusions
Compared to monovalent antibodies, multivalent VHH antibodies Nb-2E8 and Nb-3C5 showed high affinity and are potential candidates for diagnostic applications to better detect CHIKV virions in sera.
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A Metaheuristic Method for the Task Assignment Problem in Continuous-Casting Production
The steelmaking and continuous-casting (SCC) process in integrated iron and steel enterprises can be described as two stages: the upstream stage and downstream stage. Raw materials are transformed into molten steel in the upstream stage, while the downstream stage is responsible for transforming molten steel which is released at regular intervals and has a limited time for being turned into slabs. This article focuses on the task assignment problem in the downstream stage within the given information resulting from the upstream stage. This problem is formulated as a nonlinear mixed-integer programming model aimed at minimizing total tardiness within the resource constraints and time windows constraints for the tasks. An improved solution algorithm based on particle swam optimization is developed to efficiently solve the proposed model. Finally, computational experiments are implemented to evaluate the performance of the solution algorithm in terms of solution quality and computational time
Overbooking and delivery-delay-allowed strategies for container slot allocation
Due to uncontrollable markets and unreliable factors in liner container shipping industry, the deviation between the actual and estimated shipment demand of containers always exists, resulting in a waste of shipping capacity or unfulfilled shipping requirements. To tackle such a slot allocation problem, this paper proposes overbooking (OB) and delivery-postponed (DP) strategies to maximize profits. Then, two corresponding multi-period models are developed and solution algorithms are presented. Finally, several numerical experiments are carried on to show the applicability of the proposed models and solution algorithms. The results indicate that the OB and DP strategies can promote the profit
A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization
Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction