With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerab...
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With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research ***,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this *** algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for *** problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing ***,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its *** KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in *** effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.
Private Information Retrieval (PIR) protocols enable a user to retrieve a specific data item from a database while ensuring that no information about the requested item's identity is revealed. To defend against By...
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When encountering data with high dimensionality and high redundancy between features, we can select the most representative features by feature selection. In this paper, a maximum difference feature selection algorith...
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Safety equipment detection is an important application of object detection, receiving widespread attention in fields such as smart construction sites and video surveillance. Significant progress has been made in objec...
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Target recognition and tracking is an important research filed in the surveillance *** target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not ...
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Target recognition and tracking is an important research filed in the surveillance *** target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be *** this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is *** specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter *** proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving *** experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring *** root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional *** proposed algorithm has very good application value.
In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes consid...
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In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation *** paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary *** the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time *** dictionary learning is followed to separate real signals from jamming *** the learned representation,time-varying MISRJs are suppressed *** simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.
This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication...
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This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication links may be *** paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication *** links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task *** blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource *** paper uses a utility-oriented computation offloading scheme to balance costs and *** paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the *** results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.
Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent *** Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision *** intractab...
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Multi-hop reasoning for incomplete Knowledge Graphs(KGs)demonstrates excellent interpretability with decent *** Learning(RL)based approaches formulate multi-hop reasoning as a typical sequential decision *** intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance *** mainstream methods apply heuristic reward functions to counter this ***,the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object *** this end,we propose a novel adaptive Inverse Reinforcement Learning(IRL)framework for multi-hop reasoning,called AInvR.(1)To counter the missing and spurious paths,we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent’s inference trajectories;(2)to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules,we propose an adaptive negative hit reward learning mechanism based on agent’s sampling strategy;(3)to further explore diverse paths and mitigate the influence of missing facts,we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning *** results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.
Compared with traditional hydrogen storage alloys,perovskite oxide LaFeO_(3)materials are considered as one of the most promising anode materials for nickel-metal hydride batteries owing to their low cost,environmenta...
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Compared with traditional hydrogen storage alloys,perovskite oxide LaFeO_(3)materials are considered as one of the most promising anode materials for nickel-metal hydride batteries owing to their low cost,environmental friendliness,and superior temperature ***,the biggest problem faced by perovskite oxide LaFeO_(3)as an anode material for Nickel/metal hydride(Ni-MH)batteries is the low electrical conductivity and poor specific capacity,which is mainly due to the serious agglomeration phenomenon in its *** solve the above problems,lamellar LaFeO_(3)material with large specific surface area and small particle size has been synthesized by adding N,N-Dimethylformamide(DMF)and polyvinyl pyrrolidone(PVP)inhibitor materials to the *** changing the sintering temperature,the lamellar composite LaFeO_(3)material can be ***,the maximum discharge capacity of lamellar LaFeO_(3)is up to 372.1 mA h g^(-1)at the discharge current density of 60 mA g^(-1).Meanwhile,after 100 cycles,the specific discharge capacity of the lamellar LaFeO_(3)can still reach 293.1 mA h g^(-1),which is much higher than that of 98.5 mA h g^(-1)for LaFeO_(3).In addition,the kinetics of LaFeO_(3)has been investigated and the lamellar LaFeO_(3)shows excellent dynamic ***,the exchange current density I0(300 mA g^(-1))of the layered LaFeO_(3)electrode is higher than that of LaFeO_(3)(150 mA g^(-1)).Overall,this work provides insights into a structure-performance relationship for the further development of high-performance perovskite-type oxide nickel-metal hydride battery anodes.
As an important task in emotion analysis, Multimodal Emotion-Cause Pair Extraction in conversations (MECPE) aims to extract all the emotion-cause utterance pairs from a conversation. However, there are two shortcoming...
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As an important task in emotion analysis, Multimodal Emotion-Cause Pair Extraction in conversations (MECPE) aims to extract all the emotion-cause utterance pairs from a conversation. However, there are two shortcomings in the MECPE task: 1) it ignores emotion utterances whose causes cannot be located in the conversation but require contextualized inference;2) it fails to locate the exact causes that occur in vision or audio modalities beyond text. To address these issues, in this paper, we introduce a new task named Multimodal Emotion-Cause Pair Generation in Conversations (MECPG), which aims to identify the emotion utterances with their emotion categories and generate their corresponding causes in a conversation. To tackle the MECPG task, we construct a dataset based on a benchmark corpus for MECPE. We further propose a generative framework named MONICA, which jointly performs emotion recognition and emotion cause generation with a sequence-to-sequence model. Experiments on our annotated dataset show the superiority of MONICA over several competitive systems. Our dataset and source codes will be publicly released. IEEE
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