Applied Sciences-Basel最新文献

{"title":"Two Scenario-Based Heuristics for Stochastic Shift Design Problem with Task-Based Demand","authors":"Zhiying Wu, Qing-xin Chen, Ning Mao, Guoning Xu","doi":"10.3390/app131810070","DOIUrl":"https://doi.org/10.3390/app131810070","url":null,"abstract":"In this paper, we propose a deterministic shift design model with task-based demand and give the corresponding stochastic version with a probability constraint such that the shift plan designed is staffed with the workforce with a certain probability of performing all given tasks. Since we currently find no suitable methods for solving this stochastic model from the literature related to solving stochastic shift design models, we developed a single-stage heuristic method based on statistics, whose main idea is to reduce the occurrence of manpower shortage by prolonging the resource occupation time of a task, but this leads to a serious waste of resources, which is common in solving resource allocation problems with uncertain durations. To reduce the cost of wastage, we also propose a two-stage heuristic approach that is a two-stage heuristic with an evolutionary strategy. The two heuristics show their effectiveness in solving the proposed stochastic model in numerical experiments, and the two-stage heuristic significantly outperforms the one-stage heuristic in cost optimization and solution time stability.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44746541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power Drive Architectures for Industrial Hydraulic Axes: Energy-Efficiency-Based Comparative Analysis","authors":"M. Tiboni","doi":"10.3390/app131810066","DOIUrl":"https://doi.org/10.3390/app131810066","url":null,"abstract":"In hydraulic systems, energy dissipation can be significant. The pressure losses that can occur in the hydraulic circuit, which are influenced by the adopted drive architecture, result in power consumption that is often significantly higher than that required by the mechanical system. This paper presents a comparative study of the energy efficiency of five common drive architectures in industrial hydraulic axes. The analysis is applied to a variable speed and force hydraulic blanking press, a fairly common industrial system, e.g., in the manufacture of semi-finished brass products. Standard, regenerative, high–low, variable-displacement pumps and variable speed drive configurations for a fixed-displacement pump were analyzed and compared. In each case, an appropriate and optimized sizing of the different components of the system was performed, and then the energy consumption was estimated for a load cycle common to all the considered cases. The results show that the choice of the power generation architecture of the hydraulic system has a very significant impact on the energy efficiency and consequently on the operating costs and the carbon footprint. The performed quantification of the potential energy efficiency of the considered drive architectures can be very useful in helping to make energy-conscious decisions.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46682358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Peer-to-Peer PV Trading Strategies under the Influence of Network Constraints with Prosumer Sensitivity towards Network Coefficients","authors":"Sheroze Liaquat, Tanveer Hussain, Fadi Agha Kassab, B. Celik, Robert S. Fourney, T. Hansen","doi":"10.3390/app131810044","DOIUrl":"https://doi.org/10.3390/app131810044","url":null,"abstract":"With the increase in rooftop photovoltaic (PV) systems at the residential level, customers owning such renewable resources can act as a source of generation for other consumers in the same network. Peer-to-peer (P2P) energy trading refers to a local trading platform where the residential customers having excess PV power (prosumers) can interact with their neighbors without PV resources (customers) to improve the social welfare of society. However, the performance of a P2P market depends on the power system network constraints and trading strategy adopted for local energy trading. In this paper, we compare different trading strategies, i.e., the rule-based zero intelligent (ZI) strategy and the preference-based game theory (GT) approaches, for a constrained P2P platform. Quadratic trading loss and impedance-based network utilization fee models are suggested to define the network constraints for the P2P system. Additionally, a reluctance-based prosumer-sensitive model is developed to adjust the trading behavior of the participants under the heavy distribution losses/network fee. The presented results show that the suggested trading strategies enhanced the average welfare of the participants by approximately 17%. On average, the customers saved about $33.77 monthly, whereas the average monthly earnings of the prosumers were around $28.3. The ZI strategy enhanced the average monetary advantages of all the market participants by an average of 7% for a system having small distribution losses and a network fee as compared to the GT approach. Contrarily, for a system having high losses/a utilization fee, the GT approach improved the average welfare of the prosumers by around 75% compared to the ZI strategy. However, both trading strategies yielded competitive results compared to the traditional market under the standard values of network coefficients.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48197996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data","authors":"Hongyang Zhi, Baofeng Pan, Guibing Zhu","doi":"10.3390/app131810035","DOIUrl":"https://doi.org/10.3390/app131810035","url":null,"abstract":"This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47027343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systemic Literature Review of Recognition-Based Authentication Method Resistivity to Shoulder-Surfing Attacks","authors":"L. A. Adebimpe, Ian Ouii Ng, Mohd Yamani Idna Idris, Mohammed Okmi, Chin Soon Ku, T. F. Ang, L. Y. Por","doi":"10.3390/app131810040","DOIUrl":"https://doi.org/10.3390/app131810040","url":null,"abstract":"The rapid advancement of information technology (IT) has given rise to a new era of efficient and fast communication and transactions. However, the increasing adoption of and reliance on IT has led to the exposure of personal and sensitive information online. Safeguarding this information against unauthorized access remains a persistent challenge, necessitating the implementation of improved computer security measures. The core objective of computer security is to ensure the confidentiality, availability, and integrity of data and services. Among the mechanisms developed to counter security threats, authentication stands out as a pivotal defense strategy. Graphical passwords have emerged as a popular authentication approach, yet they face vulnerability to shoulder-surfing attacks, wherein an attacker can clandestinely observe a victim’s actions. Shoulder-surfing attacks present a significant security challenge within the realm of graphical password authentication. These attacks occur when an unauthorized individual covertly observes the authentication process of a legitimate user by shoulder surfing the user or capturing the interaction through a video recording. In response to this challenge, various methods have been proposed to thwart shoulder-surfing attacks, each with distinct advantages and limitations. This study thus centers on reviewing the resilience of existing recognition-based graphical password techniques against shoulder-surfing attacks by conducting a comprehensive examination and evaluation of their benefits, strengths, and weaknesses. The evaluation process entailed accessing pertinent academic resources through renowned search engines, including Web of Science, Science Direct, IEEE Xplore, ProQuest, Scopus, Springer, Wiley Online Library, and EBSCO. The selection criteria were carefully designed to prioritize studies that focused on recognition-based graphical password methods. Through this rigorous approach, 28 studies were identified and subjected to a thorough review. The results show that fourteen of them adopted registered objects as pass-objects, bolstering security through object recognition. Additionally, two methods employed decoy objects as pass-objects, enhancing obfuscation. Notably, one technique harnessed both registered and decoy objects, amplifying the security paradigm. The results also showed that recognition-based graphical password techniques varied in their resistance to different types of shoulder-surfing attacks. Some methods were effective in preventing direct observation attacks, while others were vulnerable to video-recorded and multiple-observation attacks. This vulnerability emerged due to attackers potentially extracting key information by analyzing user interaction patterns in each challenge set. Notably, one method stood out as an exception, demonstrating resilience against all three types of shoulder-surfing attacks. In conclusion, this study contributes to a comprehensive understanding of the ef","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47735022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path Planning for Autonomous Vehicles in Unknown Dynamic Environment Based on Deep Reinforcement Learning","authors":"Hui Hu, Yuge Wang, Wenjie Tong, Jiao Zhao, Yulei Gu","doi":"10.3390/app131810056","DOIUrl":"https://doi.org/10.3390/app131810056","url":null,"abstract":"Autonomous vehicles can reduce labor power during cargo transportation, and then improve transportation efficiency, for example, the automated guided vehicle (AGV) in the warehouse can improve the operation efficiency. To overcome the limitations of traditional path planning algorithms in unknown environments, such as reliance on high-precision maps, lack of generalization ability, and obstacle avoidance capability, this study focuses on investigating the Deep Q-Network and its derivative algorithm to enhance network and algorithm structures. A new algorithm named APF-D3QNPER is proposed, which combines the action output method of artificial potential field (APF) with the Dueling Double Deep Q Network algorithm, and experience sample rewards are considered in the experience playback portion of the traditional Deep Reinforcement Learning (DRL) algorithm, which enhances the convergence ability of the traditional DRL algorithm. A long short-term memory (LSTM) network is added to the state feature extraction network part to improve its adaptability in unknown environments and enhance its spatiotemporal sensitivity to the environment. The APF-D3QNPER algorithm is compared with mainstream deep reinforcement learning algorithms and traditional path planning algorithms using a robot operating system and the Gazebo simulation platform by conducting experiments. The results demonstrate that the APF-D3QNPER algorithm exhibits excellent generalization abilities in the simulation environment, and the convergence speed, the loss value, the path planning time, and the path planning length of the APF-D3QNPER algorithm are all less than for other algorithms in diverse scenarios.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48441487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication","authors":"Stylianos Karagiannis, E. Magkos, Christoforos Ntantogian, Ricardo Cabecinha, Theo Fotis","doi":"10.3390/app131810072","DOIUrl":"https://doi.org/10.3390/app131810072","url":null,"abstract":"Medical imaging plays a crucial role in modern healthcare, providing essential information for accurate diagnosis and treatment planning. The Digital Imaging and Communications in Medicine (DICOM) standard has revolutionized the storage, transmission, and sharing of medical images and related data. Despite its advantages, implementation and deployment of the DICOM protocol often suffers from incomplete understanding, leading to vulnerabilities within the healthcare ecosystem. This research paper presents an implementation of DICOM communication and the development of a practical demonstration for simulation purposes The simulation can be used for conducting cybersecurity tests in the context of DICOM communication. Overall, the simulation provides a digital environment that can help in retrieving valuable insights into the practical aspects of DICOM communication and PACS integration, serving as a valuable resource for medical imaging professionals, researchers, and developers. These research results provide practical insights, and the DICOM simulation can be used in realistic contexts to showcase a variety of security scenarios.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43053940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Double-Constrained Consensus Clustering with Application to Online Anti-Counterfeiting","authors":"Claudio Carpineto, Giovanni Romano","doi":"10.3390/app131810050","DOIUrl":"https://doi.org/10.3390/app131810050","url":null,"abstract":"Semi-supervised consensus clustering is a promising strategy to compensate for the subjectivity of clustering and its sensitivity to design factors, with various techniques being recently proposed to integrate domain knowledge and multiple clustering partitions. In this article, we present a new approach that makes double use of domain knowledge, namely to build the initial partitions, as well as to combine them. In particular, we show how to model and integrate must-link and cannot-link constraints into the objective function of a generic consensus clustering (CC) framework that maximizes the similarity between the consensus partition and the input partitions, which have, in turn, been enriched with the same constraints. In addition, borrowing from the theory of functional dependencies, the integrated framework exploits the notions of deductive closure and minimal cover to take full advantage of the logical implication between constraints. Using standard UCI benchmarks, we found that the resulting algorithm, termed CCC double-constrained consensus clustering), was more effective than plain CC at combining base-constrained partitions, with an average performance improvement of 5.54%. We then argue that CCC is especially well-suited for profiling counterfeit e-commerce websites, as constraints can be acquired by leveraging specific domain features, and demonstrate its potential for detecting affiliate marketing programs. Taken together, our experiments suggest that CCC makes the process of clustering more robust and able to withstand changes in clustering algorithms, datasets, and features, with a remarkable improvement in average performance.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47327134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Retail Transactions: A Data-Driven Recommendation Using Modified RFM Analysis and Association Rules Mining","authors":"Angela Hsiang-Ling Chen, Sebastian Gunawan","doi":"10.3390/app131810057","DOIUrl":"https://doi.org/10.3390/app131810057","url":null,"abstract":"Retail transactions have become an integral part of the economic cycle of every country and even on a global scale. Retail transactions are a trade sector that has the potential to be developed continuously in the future. This research focused on building a specified and data-driven recommendation system based on customer-purchasing and product-selling behavior. Modified RFM analysis was used by adding two variables, namely periodicity and customer engagement index; clustering algorithm such as K-means clustering and Ward’s method; and association rules to determine the pattern of the cause–effect relationship on each transaction and four types of classifiers to apply and to validate the recommendation system. The results showed that based on customer behavior, it should be split into two groups: loyal and potential customers. In contrast, for product behavior, it also comprised three groups: bestseller, profitable, and VIP product groups. Based on the result, K-nearest neighbor is the most suitable classifier with a low chance of overfitting and a higher performance index.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45662579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs","authors":"Haemin Jung, Heesung Park, Kwangyon Lee","doi":"10.3390/app131810041","DOIUrl":"https://doi.org/10.3390/app131810041","url":null,"abstract":"Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45018053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}