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  • MDPI AG  (25)
  • 1
    Online Resource
    Online Resource
    Simulation and Experiment Study on Cone End Billet Method in Upsetting Billet with a Large Height-to-Diameter Ratio
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 17 ( 2023-08-23), p. 9523-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 17 ( 2023-08-23), p. 9523-
    Abstract: A novel upsetting method, called Cone End Billet Upsetting (CEBU), is proposed in this paper to control bulging during the upsetting of large height-to-diameter ratio (LHDR) billets. This new upsetting method is mainly characterized by prefabricating a conical shape at the billet end, which aims to reduce the friction effect between the billet end and the anvil. In order to validate CEBU, the metal flow characteristics during upsetting of LHDR billets with traditional upsetting (TU) and CEBU were analyzed and compared by the finite element method. Experiments were also carried out to examine the deformation characteristics and microstructure of pure copper samples. The results show that, compared with TU, CEBU has a great advantage in restraining bulging and enhancing the compaction effect of upsetting. Meanwhile, bulging can be eliminated in CEBU with a 50% reduction ratio. In addition, aided by the cone end, the metal flow is no longer sensitive to the friction effect at the billet end. From the point of view of restraining bulging, a small taper angle is necessary prior to use. Furthermore, to avoid instability deformation, the height-to-diameter ratio of the billet should be below 3.0. CEBU is effective in suppressing the generation of bulging, but it also increases the pre-forming process for the end of the billet. The study on CEBU in this article is under laboratory conditions, and exploring the industrial application of CEBU will be the focus of our future research.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13179523
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 2
    Online Resource
    Online Resource
    HeteEdgeWalk: A Heterogeneous Edge Memory Random Walk for Heterogeneous Information Network Embedding
    MDPI AG ; 2023
    In:  Entropy Vol. 25, No. 7 ( 2023-06-29), p. 998-
    Details
    In: Entropy, MDPI AG, Vol. 25, No. 7 ( 2023-06-29), p. 998-
    Abstract: Most Heterogeneous Information Network (HIN) embedding methods use meta-paths to guide random walks to sample from HIN and perform representation learning in order to overcome the bias of traditional random walks that are more biased towards high-order nodes. Their performance depends on the suitability of the generated meta-paths for the current HIN. The definition of meta-paths requires domain expertise, which makes the results overly dependent on the meta-paths. Moreover, it is difficult to represent the structure of complex HIN with a single meta-path. In a meta-path guided random walk, some of the heterogeneous structures (e.g., node type(s)) are not among the node types specified by the meta-path, making this heterogeneous information ignored. In this paper, HeteEdgeWalk, a solution method that does not involve meta-paths, is proposed. We design a dynamically adjusted bidirectional edge-sampling walk strategy. Specifically, edge sampling and the storage of recently selected edge types are used to better sample the network structure in a more balanced and comprehensive way. Finally, node classification and clustering experiments are performed on four real HINs with in-depth analysis. The results show a maximum performance improvement of 2% in node classification and at least 0.6% in clustering compared to baselines. This demonstrates the superiority of the method to effectively capture semantic information from HINs.
    Type of Medium: Online Resource
    ISSN: 1099-4300
    URL: Article
    DOI: 10.3390/e25070998
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2014734-X
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  • 3
    Online Resource
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    Cache-Based Privacy Protection Scheme for Continuous Location Query
    MDPI AG ; 2023
    In:  Entropy Vol. 25, No. 2 ( 2023-01-19), p. 201-
    Details
    In: Entropy, MDPI AG, Vol. 25, No. 2 ( 2023-01-19), p. 201-
    Abstract: Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user’s demand, we use a variable-order Markov model to predict the user’s future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users’ location privacy.
    Type of Medium: Online Resource
    ISSN: 1099-4300
    URL: Article
    DOI: 10.3390/e25020201
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2014734-X
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  • 4
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    A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 13 ( 2023-07-05), p. 6176-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 13 ( 2023-07-05), p. 6176-
    Abstract: Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to software-defined networks (SDNs). This paper proposes a feature-engineering- and machine-learning-based approach to detect DDoS attacks in SDNs. First, the CSE-CIC-IDS2018 dataset was cleaned and normalized, and the optimal feature subset was found using an improved binary grey wolf optimization algorithm. Next, the optimal feature subset was trained and tested in Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost machine learning algorithms, from which the best classifier was selected for DDoS attack detection and deployed in the SDN controller. The results show that RF performs best when compared across several performance metrics (e.g., accuracy, precision, recall, F1 and AUC values). We also explore the comparison between different models and algorithms. The results show that our proposed method performed the best and can effectively detect and identify DDoS attacks in SDNs, providing a new idea and solution for the security of SDNs.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23136176
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 5
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    Online Resource
    Location Privacy-Preserving Query Scheme Based on the Moore Curve and Multi-User Cache
    MDPI AG ; 2022
    In:  Information Vol. 13, No. 9 ( 2022-09-06), p. 417-
    Details
    In: Information, MDPI AG, Vol. 13, No. 9 ( 2022-09-06), p. 417-
    Abstract: With the rapid development of the Internet of Things, location-based services have emerged in many social and business fields. In obtaining the service, the user needs to transmit the query data to an untrusted location service provider for query and then obtain the required content. Most existing schemes tend to protect the user’s location privacy information while ignoring the user’s query privacy. This paper proposes a secure and effective query privacy protection scheme. The multi-user cache is used to store historical query results, reduce the number of communications between users and untrusted servers, and introduce trust computing for malicious users in neighbor caches, thereby reducing the possibility of privacy leakage. When the cache cannot meet the demand, the user’s location coordinates are converted using the Moore curve, processed using encryption technology, and sent to the location service provider to prevent malicious entities from accessing the transformed data. Finally, we simulate and evaluate the scheme on real datasets, and the experimental results demonstrate the safety and effectiveness of the scheme.
    Type of Medium: Online Resource
    ISSN: 2078-2489
    URL: Article
    DOI: 10.3390/info13090417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2599790-7
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  • 6
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    MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 12 ( 2023-06-15), p. 2689-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 12 ( 2023-06-15), p. 2689-
    Abstract: This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they are located and how much computational power they have. At the same time, we considered an important issue: the heterogeneity of different client computing resources (such as device processor computing power) and server communication channels (which may be limited by geography or device). For this situation, a client and edge server selection algorithm (CESA) based on a greedy algorithm is proposed in this paper. Given resource constraints, CESA aims to select as many clients and edge servers as possible to participate in the model computation in order to improve the accuracy of the model. The simulation results show that, when the number of clients is high, the multi-level federated edge learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm. Meanwhile, the CESA is able to aggregate more clients for training in the same amount of time compared to the baseline algorithm, improving model training accuracy.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12122689
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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  • 7
    Online Resource
    Online Resource
    Changes in Physicochemical Properties and Volatiles of Kiwifruit Pulp Beverage Treated with High Hydrostatic Pressure
    MDPI AG ; 2020
    In:  Foods Vol. 9, No. 4 ( 2020-04-12), p. 485-
    Details
    In: Foods, MDPI AG, Vol. 9, No. 4 ( 2020-04-12), p. 485-
    Abstract: Physicochemical properties and volatiles of kiwifruit pulp beverage treated with high hydrostatic pressure (HHP, 400–600 MPa/5–15 min) were investigated during 40-day refrigerated storage. Compared with heat treatment (HT), HHP ranged from 400–500 MPa was superior in retaining vitamin C, fresh-like color and volatiles, while soluble solids content and pH were not affected significantly. Furthermore, HHP improved brightness and inhibited browning of kiwifruit pulp beverage. Samples treated at 400 MPa for 15 min showed significantly higher vitamin C content and lower ∆E values over 40 days than heat-treated kiwifruit pulp beverage. The total content of alcohols, esters, acids, and ketones gradually increased, whereas the total aldehydes content decreased during storage. Interestingly, HHP treatment at 500 MPa for 15 min mostly retained important characteristic volatiles including hexanal and (E)-2-hexenal, indicating this treatment was more conducive to preserve the original fruity, fresh, grassy and green notes of kiwifruit pulp beverage than HT.
    Type of Medium: Online Resource
    ISSN: 2304-8158
    URL: Article
    DOI: 10.3390/foods9040485
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704223-6
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  • 8
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    Numerical Simulation of Ship Oil Spill in Arctic Icy Waters
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 4 ( 2020-02-19), p. 1394-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 4 ( 2020-02-19), p. 1394-
    Abstract: This paper presents a three-dimensional numerical simulation model of an oil spill for application in emergency treatment methods under icy water conditions. The combined effects of wind, wave, current and ice implemented in our model correspond to Arctic Ocean conditions. A discrete element method combined with an overset grid was adopted to track the trajectory movements of oil film with medium-density ice floes and simulate the flow field of moving ice of large displacement in six degrees of freedom (6DOF). The probability of oil spill area extensions were estimated by a response surface method (RSM). Results showed reduced risk of pollution in icy water conditions and greater drift action of oil film. Accordingly, the spraying location and quantity of oil-dispersant could be rapidly specified.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10041394
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 9
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    Comparison of Biomarkers of Exposure in a Controlled Study of Smokers Switched from Conventional Cigarettes to Heated Tobacco Products
    MDPI AG ; 2023
    In:  Toxics Vol. 11, No. 10 ( 2023-09-28), p. 816-
    Details
    In: Toxics, MDPI AG, Vol. 11, No. 10 ( 2023-09-28), p. 816-
    Abstract: The heated tobacco product (HTP) heats rather than burns tobacco to release an aerosol with significantly fewer toxicants than conventional cigarette smoke and has received global attention in recent years. To investigate whether changes in biomarkers of exposure could be detected after switching from conventional cigarettes (CCs) to HTPs, 224 subjects from four cities in China participated in this study. Nine biomarkers containing tobacco-specific nitrosamines (TSNAs), volatile organic compounds (VOCs), and the biomarkers for acrolein and crotonaldehyde were determined by UPLC-MS/MS. The levels of the sum of nine biomarkers in CCs were 5.4 and 5.2 times higher than in an Original-HTP and Menthol-HTP, respectively. Among the nine biomarkers, 3HPMA and 3HMPMA accounted for the highest proportions. Switching from CCs to HTPs is good for both men and women because the changes in each biomarker in urine samples were the same in men and women. Among all the subjects, subjects aged 20–39 years had the greatest reduction in biomarker residues in urine. The findings of the present study provided useful information for the health risk research of HTPs in China.
    Type of Medium: Online Resource
    ISSN: 2305-6304
    URL: Article
    DOI: 10.3390/toxics11100816
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2733883-6
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  • 10
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    C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 12 ( 2023-06-14), p. 3103-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 12 ( 2023-06-14), p. 3103-
    Abstract: The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15123103
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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