Applied Soft Computing最新文献_第7页

Geometrical invariant generative invisible hyperlinks based on feature points

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-13 DOI: 10.1016/j.asoc.2025.112959

Zecheng Peng , Bingwen Feng , Xiaotao Xu , Jilian Zhang , Donghong Cai , Wei Lu

{"title":"Geometrical invariant generative invisible hyperlinks based on feature points","authors":"Zecheng Peng , Bingwen Feng , Xiaotao Xu , Jilian Zhang , Donghong Cai , Wei Lu","doi":"10.1016/j.asoc.2025.112959","DOIUrl":"10.1016/j.asoc.2025.112959","url":null,"abstract":"<div><div>To enhance the visual diversity of Quick Response (QR) codes while ensuring their robust decoding capabilities, this paper introduces an innovative invisible hyperlink generation system. The system can use a message sequence to directly generate a hyperlink image. By harnessing the latent space of a suggested feature point generation network, the system extends the robustness of image feature points to the hyperlink images it generates. Specially, an image generation network is first designed to synthesize high-quality images based on feature point data. Subsequently, a set of lightweight message encoder and decoder are introduced to embed message bits into the latent space of the image generation network. Experimental results show that the proposed invisible hyperlink generation system can successfully generate images containing hyperlinks, exhibiting remarkable resilience against common signal processing and geometric distortions. It harbors diverse potential applications, encompassing website URLs, contact information, product specifics, and numerous other use cases.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112959"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive large neighborhood search incorporating mixed-integer linear programming for electric vehicle routing problem with mobile charging and nonlinear battery degradation

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-13 DOI: 10.1016/j.asoc.2025.112988

Senyan Yang , Ruiyan Zhang , Ying Ma , Xingquan Zuo

{"title":"Adaptive large neighborhood search incorporating mixed-integer linear programming for electric vehicle routing problem with mobile charging and nonlinear battery degradation","authors":"Senyan Yang , Ruiyan Zhang , Ying Ma , Xingquan Zuo","doi":"10.1016/j.asoc.2025.112988","DOIUrl":"10.1016/j.asoc.2025.112988","url":null,"abstract":"<div><div>The limited driving range and short battery life are obstacles to the widespread adoption of electric vehicles in urban logistics. This study proposes an electric vehicle routing problem with time window, mobile charging, and nonlinear battery degradation. Mobile charging vehicles (MCVs) can be flexibly scheduled to charge the electric delivery vehicles (EDVs) at customer locations, reducing the electricity consumption caused by the detours to the charging stations. The proposed problem is formulated into an arc-based model that incorporates nonlinear battery degradation costs associated with State of Charge (SOC) and charging strategies, thereby enhancing the complexity of the spatio-temporal synchronization mechanism. Constraining a lower SOC can mitigate the battery degradation of EDVs, but it leads to increased charging demands and makes searching for feasible routing solutions more challenging due to the interdependence between MCVs and EDVs. A hybrid adaptive large neighborhood search heuristic algorithm is developed. Dynamic programming is embedded in the algorithm framework to devise charging schemes considering nonlinear battery degradation for the given EDVs’ routes. A mixed-integer linear programming model is formulated to select the combination of labels with continuous charging decisions and design MCVs’ routes. Extensive numerical experiments are conducted to verify the proposed model and algorithm. Experimental results indicate considering battery degradation in the objectives significantly improves the total system costs by optimizing the SOC and charging quantity. Mobile charging can be an alternative for constructing fixed charging facilities due to the charging flexibility of MCVs. The performance of our algorithm is demonstrated through both large-scale instances and a real-world case study on urban logistics.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 112988"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent modeling for indoor fire risk prediction during evacuation based on cellular automata and artificial neural network

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-13 DOI: 10.1016/j.asoc.2025.113013

Peng Lu

{"title":"Multi-agent modeling for indoor fire risk prediction during evacuation based on cellular automata and artificial neural network","authors":"Peng Lu","doi":"10.1016/j.asoc.2025.113013","DOIUrl":"10.1016/j.asoc.2025.113013","url":null,"abstract":"<div><div>Fire cases have always posed threats to human lives and property safety, and new approaches have been developed to investigate how people behave during the fire process. Understanding the underlying mechanism under specific scenarios and conditions is critical to find possible ways of reducing social losses. Here, we propose a coupled model that combines FDS and CA, to assess fire risks in a multi-story dormitory building at a university. For this real target case, the settings of automatic sprinklers and temperature alarms will be considered in our coupled model. The aim is to investigate how pedestrians behave under the fire emergencies and how fire safety facilities (exits) shape final evacuation outcomes. To analysis the final outcomes and related factors, we use Event Tree and BP neural network methods to assess and predict individual risk levels. It suggests that controlling the number of people in each dormitory will effectively reduce the fire risk, and the existence of safety facilities can significantly contain fire risks. Early fire warning systems and quick response times are critical to reduce casualties during the evacuation process. Individual risk levels can be efficiently calculated by Event Tree method, and BP neural network can accurately predict fire risk levels. By integrating technologies such as FDS, CA, ETA, and BP neural networks, our model can effectively simulate the dynamic process of the fire evacuation while accurately predicting the fire risks, which establishes an effective link between environmental factors and fire risk assessment. This provides a methodological reference for future fire risk assessment research.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 113013"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent reinforcement learning system framework based on topological networks in Fourier space

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-13 DOI: 10.1016/j.asoc.2025.112986

Licheng Sun, Ao Ding, Hongbin Ma

{"title":"Multi-agent reinforcement learning system framework based on topological networks in Fourier space","authors":"Licheng Sun, Ao Ding, Hongbin Ma","doi":"10.1016/j.asoc.2025.112986","DOIUrl":"10.1016/j.asoc.2025.112986","url":null,"abstract":"<div><div>Currently, multi-agent reinforcement learning (MARL) has been applied to various domains such as communications, network management, power systems, and autonomous driving, showcasing broad application scenarios and significant research potential. However, in complex decision-making environments, agents that rely solely on temporal value functions often struggle to capture and extract hidden features and dependencies within long sequences in multi-agent settings. Each agent’s decisions are influenced by a sequence of prior states and actions, leading to complex spatiotemporal dependencies that are challenging to analyze directly in the time domain. Addressing these challenges requires a paradigm shift to analyze such dependencies from a novel perspective. To this end, we propose a Multi-Agent Reinforcement Learning system framework based on Fourier Topological Space from the foundational level. This method involves transforming each agent’s value function into the frequency domain for analysis. Additionally, we design a lightweight weight calculation method based on historical topological relationships in the Fourier topological space. This addresses issues of instability and poor reproducibility in attention weights, along with various other interpretability challenges. The effectiveness of this method is validated through experiments in complex environments such as the StarCraft Multi-Agent Challenge (SMAC) and Google Football. Furthermore, in the Non-monotonic Matrix Game, our method successfully overcame the limitations of non-monotonicity, further proving its wide applicability and superiority. On the application level, the proposed algorithm is also applicable to various multi-agent system domains, such as robotics and factory robotic arm control. The algorithm can control each joint in a coordinated manner to accomplish tasks such as enabling a robot to stand upright or controlling the movements of robotic arms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112986"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A three-way decision-based model for occupational risk assessment and classification in the healthcare industry

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-13 DOI: 10.1016/j.asoc.2025.112991

Ran Liu , Hu-Chen Liu , Qi-Zhen Zhang , Hua Shi

{"title":"A three-way decision-based model for occupational risk assessment and classification in the healthcare industry","authors":"Ran Liu , Hu-Chen Liu , Qi-Zhen Zhang , Hua Shi","doi":"10.1016/j.asoc.2025.112991","DOIUrl":"10.1016/j.asoc.2025.112991","url":null,"abstract":"<div><div>Nowadays, occupational health and safety risk assessment (OHSRA) has gained more importance since occupational hazards can cause loss of life, injuries, delays, and cost overruns in an organization. The OHSRA is a critical activity for identifying, analyzing and reducing the potential occupational hazards arising from workplace for corrective actions. In this study, a new OHSRA model is proposed for the risk assessment and classification of occupational hazards by utilizing the criteria importance through inter-criteria correlation (CRITIC) method and three-way decision (TWD). First, the 2-tuple linguistic variables are utilized to express the complex and uncertain risk assessments of occupational hazards provided by experts. Second, an extended CRITIC method is employed to compute the weights of risk criteria by considering their interactions. Then the TWD is improved to determine the risk classifications of occupational hazards by considering their correlations. Finally, a practical case in the healthcare industry is provided to illustrate the feasibility and strengths of the proposed OHSRA model. The results show that the proposed OHSRA model can generate more credible risk classifications of occupational hazards and offer a flexible way for analyzing the risk of occupational hazards.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112991"},"PeriodicalIF":7.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Target temperature field prediction via a thermodynamic knowledge-based artificial neural network

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-12 DOI: 10.1016/j.asoc.2025.112972

Jincheng Chen , Feiding Zhu , Yuge Han , Dengfeng Ren

{"title":"Target temperature field prediction via a thermodynamic knowledge-based artificial neural network","authors":"Jincheng Chen , Feiding Zhu , Yuge Han , Dengfeng Ren","doi":"10.1016/j.asoc.2025.112972","DOIUrl":"10.1016/j.asoc.2025.112972","url":null,"abstract":"<div><div>With the rapid development of artificial intelligence, representation-supervised neural networks have been widely used in the fast solution of physical field. However, a large number of temperature prediction networks do not take environmental parameters into account, or only use parameters as simple input conditions, which greatly reduces the accuracy of their results. This paper proposes an accurate and low-cost method for adding the conditional parameters to intelligent prediction networks. A novel parameter encoder block is designed based on the heat transfer theory achieving thermodynamic knowledge-based parameter feature extraction. Meanwhile, an improved method for inputting time condition is proposed to characterize the temporal characteristics, which can reduce the requirement of dataset for transient temperature prediction, compared with LSTM. In addition, a thermal loss for temperature images is introduced to accelerate the convergence process in the model. Moreover, a CycleGAN-based temperature prediction network (CBTPN) is constructed for fast temperature prediction of a cube or different tanks. Temperature or infrared images predicted by our network exhibit MAE of less than 2.33 % and SSIM of more than 80.21 %. By embedding physical mechanisms into neural networks, this study this study pioneers a structured approach to refining physical parameters into thermodynamic knowledge-based signals for improved image generation, addressing the accuracy and efficiency limitations of data-driven algorithms caused by their insufficient understanding of parameter mechanisms. Finally, parameter cognitive evaluation proves that our approach can not only recognize the accurate semantics of heat transfer parameters, but also sense the meteorological laws.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112972"},"PeriodicalIF":7.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Saccade inspired Attentive Visual Patch Transformer for image sentiment analysis

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-12 DOI: 10.1016/j.asoc.2025.112963

Jing Zhang, Jixiang Zhu, Han Sun, Xinzhou Zhang, Jiangpei Liu

{"title":"Saccade inspired Attentive Visual Patch Transformer for image sentiment analysis","authors":"Jing Zhang, Jixiang Zhu, Han Sun, Xinzhou Zhang, Jiangpei Liu","doi":"10.1016/j.asoc.2025.112963","DOIUrl":"10.1016/j.asoc.2025.112963","url":null,"abstract":"<div><div>The generation of image-evoked emotion is usually regarded as a transient process in the image sentiment analysis. However, according to the saccade mechanism of the human visual system, the evoked emotion generated during the saccade process changes over time and attention. Based on above analysis, we propose an Attentive Visual Patch Transformer (AVPT), using visual attention sequence to represent the sentiment context of images and predict the possible distribution of sentiment. In AVPT, the spatial structure in the form of patches are reconstructed and reorganized by visual attention shift sequentially. Simultaneously, the temporal characteristics of attention shift are introduced to the relative position encoding, and merged in a self-attention manner to form a spatial–temporal process similarly to the human visual system. Specifically, we propose a sequence attention shift module to simulate the saccade process, which obtains sequence attention and reduces the computational effort by group attentive convolutional gate recurrent unit. Then, a spatial–temporal correlation encoder module is proposed to encode temporal attention with spatial visual features and obtain the sequential visual features of saccade. Finally, a self-attention fusion module is used to extract the correlation hidden in the relative encoding features. Our proposed AVPT achieves excellent performance on visual sentiment distribution prediction and is comparable to state-of-the-art methods, as demonstrated by extensive experiments on the Flickr_LDL and Twitter_LDL datasets.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112963"},"PeriodicalIF":7.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unmanned aerial vehicle takeoff point search algorithm with information sharing strategy of random trees for multi-area coverage task

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-11 DOI: 10.1016/j.asoc.2025.112970

Shouwen Yao, Xiaoyu Wang, Siqi Huang, Renjie Xu, Yinghua Zhao

{"title":"Unmanned aerial vehicle takeoff point search algorithm with information sharing strategy of random trees for multi-area coverage task","authors":"Shouwen Yao, Xiaoyu Wang, Siqi Huang, Renjie Xu, Yinghua Zhao","doi":"10.1016/j.asoc.2025.112970","DOIUrl":"10.1016/j.asoc.2025.112970","url":null,"abstract":"<div><div>This study proposes a novel approach to optimize full-coverage search in distributed task areas using a single Unmanned Ground Vehicle (UGV) to deliver an Unmanned Aerial Vehicle (UAV) to the takeoff points of each task area along the shortest possible path. Unlike the traditional Traveling Salesman Problem (TSP), task areas are not fixed nodes, and obstacles must be considered. To address these challenges, a probability-based Rapid-exploration Random Tree (<em>p</em>-RRT) with an information-sharing strategy is introduced, significantly improving the efficiency of locating takeoff points in complex environments. A dual optimization method further reduces the number of nodes and path length planned by the D* algorithm, achieving up to an 80 % reduction in nodes and improving path efficiency. Additionally, a simulated annealing (SA) algorithm optimizes the connection sequence of takeoff points, reducing total path length by 35.05 % compared to the initial path and 22.66 % compared to the traditional Random Sampling Method (RSM). Experiments confirm that the proposed algorithms can effectively enhance UGV-UAV collaboration with reducing path complexity and improving energy efficiency, and thus streamline multi-area coverage tasks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112970"},"PeriodicalIF":7.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning in produce perception of harvesting robots: A comprehensive review

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-11 DOI: 10.1016/j.asoc.2025.112971

Yuhao Jin , Xiaoyu Xia , Qizhong Gao , Yong Yue , Eng Gee Lim , Prudence Wong , Weiping Ding , Xiaohui Zhu

引用次数: 0

Transferable adversarial attacks against face recognition using surrogate model fine-tuning

IF 7.2 1区计算机科学

Applied Soft Computing Pub Date : 2025-03-11 DOI: 10.1016/j.asoc.2025.112983

Yasmeen M. Khedr , Xin Liu , Haobo Lu , Kun He

{"title":"Transferable adversarial attacks against face recognition using surrogate model fine-tuning","authors":"Yasmeen M. Khedr , Xin Liu , Haobo Lu , Kun He","doi":"10.1016/j.asoc.2025.112983","DOIUrl":"10.1016/j.asoc.2025.112983","url":null,"abstract":"<div><div>Deep Neural Networks have significantly advanced Face Recognition performance yet remain susceptible to adversarial attacks, posing significant security and user privacy threats in real-world applications. In recent years, black box attacks have attracted wide attention to craft highly transferable adversarial examples by training surrogate models. However, most of these methods primarily depend on stealing knowledge by accessing the soft label from the target model using either synthetic training data or data free without awareness of the knowledge type, which can affect the improvement of transferability between the surrogate and the target models. Additionally, these attacks still need to improve the surrogate model’s accuracy without using many queries. To this end, we propose Tune2Transfer, a novel attack method that enhances adversarial transferability by fine-tuning the surrogate model with different types of knowledge with limited queries on the target model by the hard label only. Specifically, it collects a small face image dataset, considering the adversary’s limited knowledge. To overcome the challenge of knowledge type, Tune2Transfer imposes three sampling assumptions: clean images only, the perturbed images, or combining both, generating images on the surrogate model, and then feeding them to the target model to obtain the hard label. The perturbed images are generated by perturbing them using the Covariance Matrix Adaptation Evolution Strategy or Momentum Iteration Fast Gradient Sign Method. Besides, we leverage pre-trained models to fine-tune surrogate models to avoid large queries. In this way, we could leverage knowledge transferred from the target model, resulting in superior transferability. Extensive experiments conducted on two typical datasets demonstrate the efficacy of Tune2Transfer, increasing the attack success rates significantly.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"174 ","pages":"Article 112983"},"PeriodicalIF":7.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0