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  • MDPI AG  (1,259,853)
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  • MDPI AG  (1,259,853)
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  • 1
    Online Resource
    Online Resource
    Emotion Recognition Based on the Structure of Narratives
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-11), p. 919-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-11), p. 919-
    Abstract: One important application of natural language processing (NLP) is the recognition of emotions in text. Most current emotion analyzers use a set of linguistic features such as emotion lexicons, n-grams, word embeddings, and emoticons. This study proposes a new strategy to perform emotion recognition, which is based on the homologous structure of emotions and narratives. It is argued that emotions and narratives share both a goal-based structure and an evaluation structure. The new strategy was tested in an empirical study with 117 participants who recounted two narratives about their past emotional experiences, including one positive and one negative episode. Immediately after narrating each episode, the participants reported their current affective state using the Affect Grid. The goal-based structure and evaluation structure of the narratives were analyzed with a hybrid method. First, a linguistic analysis of the texts was carried out, including tokenization, lemmatization, part-of-speech tagging, and morphological analysis. Second, an extensive set of rule-based algorithms was used to analyze the goal-based structure of, and evaluations in, the narratives. Third, the output was fed into machine learning classifiers of narrative structural features that previously proved to be effective predictors of the narrator’s current affective state. This hybrid procedure yielded a high average F1 score (0.72). The results are discussed in terms of the benefits of employing narrative structure analysis in NLP-based emotion recognition.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040919
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 2
    Online Resource
    Online Resource
    A Prognosis Technique Based on Improved GWO-NMPC to Improve the Trajectory Tracking Control System Reliability of Unmanned Underwater Vehicles
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-12), p. 921-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-12), p. 921-
    Abstract: The dynamics model of the unmanned underwater vehicle (UUV) system is highly nonlinear, multi-degree-of-freedom, strongly coupled, and time-varying. Its motion control has been a complex problem due to the unknown information about and the uncertainty of the working environment. To improve the performance and reliability of UUV trajectory tracking control, a trajectory tracking method based on nonlinear model predictive control is designed, and an improved gray wolf optimization (IGWO) is proposed for the optimization of nonlinear model predictive control. The convergence factor of IGWO is designed as a nonlinear attenuation function, and the memory function is added to the position update equation to enhance the effect of trajectory tracking control. Through the simulation in the ROS environment, the influence of the convergence factor on the convergence rate of trajectory tracking error and tracking control performance is obtained. By comparing the tracking effects of several groups of reference trajectories, it is shown that the proposed method is universally applicable and effective to the trajectory tracking control of UUV. Compared with traditional gray wolf optimization (GWO), SQP, and other optimization algorithms, the reliability of the proposed method for UUV trajectory tracking control is demonstrated.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040921
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 3
    Online Resource
    Online Resource
    AdVulCode: Generating Adversarial Vulnerable Code against Deep Learning-Based Vulnerability Detectors
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-13), p. 936-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-13), p. 936-
    Abstract: Deep learning-based vulnerability detection models have received widespread attention; however, these models are susceptible to adversarial attack, and adversarial examples are a primary research direction to improve the robustness of the models. There are three main categories of adversarial example generation methods for source code tasks: changing identifier names, adding dead code, and changing code structure. However, these methods cannot be directly applied to vulnerability detection. Therefore, we propose the first study of adversarial attack on vulnerability detection models. Specifically, we utilize equivalent transformations to generate candidate statements and introduce an improved Monte Carlo tree search algorithm to guide the selection of candidate statements to generate adversarial examples. In addition, we devise a black-box approach that can be applied to widespread vulnerability detection models. The experimental results show that our approach achieves attack success rates of 16.48%, 27.92%, and 65.20%, respectively, in three vulnerability detection models with different levels of granularity. Compared with the state-of-the-art source code attack method ALERT, our method can handle models with identifier name mapping, and our attack success rate is 27.59% higher on average than ALERT.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040936
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    Online Resource
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 4
    Online Resource
    Online Resource
    FESSD: Feature Enhancement Single Shot MultiBox Detector Algorithm for Remote Sensing Image Target Detection
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-14), p. 946-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-14), p. 946-
    Abstract: Automatic target detection of remote sensing images (RSI) plays an important role in military surveillance and disaster monitoring. The core task of RSI target detection is to judge the target categories and precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for RSI with complex backgrounds. This study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection to achieve accurate detection of different categories targets. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The experimental results show that the mAP value of the proposed method reaches 81.9% and 81.2% on SD-RSI and DIOR datasets, which is superior to other compared state-of-the-art methods.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040946
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 5
    Online Resource
    Online Resource
    MDP-Based MAC Protocol for WBANs in Edge-Enabled eHealth Systems
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-14), p. 947-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-14), p. 947-
    Abstract: In recent years, eHealth systems based on the Internet of Things (IoT) have attracted considerable attention. The wireless body area network (WBAN) is an essential technology of eHealth systems. A major challenge in WBAN is the design of the medium access control (MAC) protocol, which plays a significant role in avoiding collisions, enhancing the energy efficiency, maximizing the network life, and improving the quality of service (QoS) as well as the quality of experience (QoE). In this study, we apply the mobile edge computing (MEC) network architecture to an eHealth system and design a multi-channel MAC protocol for WBAN based on the Markov decision process (MDP). In this protocol, the channel condition and the reward value are considered. By continuously interacting with the environment, the optimal channel resource allocation strategy is generated. Simulation results indicate that the proposed WBAN MAC protocol can adaptively assign different channels to the sensor nodes for data transmission, thereby reducing the collision rate, decreasing the energy consumption, improving the channel utilization, and enhancing the system throughput and QoE.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040947
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Open Access Request
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 6
    Online Resource
    Online Resource
    A Scientometric Analysis of Deep Learning Approaches for Detecting Fake News
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-14), p. 948-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-14), p. 948-
    Abstract: The unregulated proliferation of counterfeit news creation and dissemination that has been seen in recent years poses a constant threat to democracy. Fake news articles have the power to persuade individuals, leaving them perplexed. This scientometric study examined 569 documents from the Scopus database between 2012 and mid-2022 to look for general research trends, publication and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in publications since 2016, and this dissemination of fake news is still an issue from a global perspective. Thematic analysis of papers reveals that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped, while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute niche areas. Furthermore, the results show that China and the USA have the strongest international collaboration, despite India writing more articles. This paper also examines the current state of the art in deep learning techniques for fake news detection, with the goal of providing a potential roadmap for researchers interested in undertaking research in this field.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040948
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    Online Resource
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 7
    Online Resource
    Online Resource
    High Performance Network Intrusion Detection System Using Two-Stage LSTM and Incremental Created Hybrid Features
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-15), p. 956-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-15), p. 956-
    Abstract: Currently, most network intrusion detection systems (NIDSs) use information about an entire session to detect intrusion, which has the fatal disadvantage of delaying detection. To solve this problem, studies have been proposed to detect intrusions using only some packets belonging to the session but have limited effectiveness in increasing the detection performance compared to conventional methods. In addition, space complexity is high because all packets used for classification must be stored. Therefore, we propose a novel NIDS that requires low memory storage space and exhibits high detection performance without detection delay. The proposed method does not need to store packets for the current session and uses only some packets, as in conventional methods, but achieves very high detection performance. Through experiments, it was confirmed that the proposed NIDS uses only a small memory of 25.8% on average compared to existing NIDSs by minimizing memory consumption for feature creation, while its intrusion detection performance is equal to or higher than those of existing ones. As a result, this method is expected to significantly help increase network safety by overcoming the disadvantages of machine-learning-based NIDSs using existing sessions and packets.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040956
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    Online Resource
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 8
    Online Resource
    Online Resource
    Joint Spectrum and Power Allocation with User Association for 5G/Wi-Fi Coexisting Millimeter Wave Networks
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-15), p. 959-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-15), p. 959-
    Abstract: With the exponential growth of mobile data traffic, the deployment of a large number of devices in the hot-spot gathering scenario has brought great challenges to the current wireless communication network. Considering that the user service latency of unidirectional data offloading scheme is still unacceptable when 5G and Wi-Fi coexist on the unlicensed 60 GHz band, we investigate a bidirectional data offloading scheme with resource allocation in this study. More specifically, aggregation nodes (ANs) are deployed in the coverage of Wi-Fi AP to receive multi-user data in parallel in order to reduce the collision probability of transmitted packets. Then, we formulate an optimization problem aiming to maximize the sum rate through spectrum and power allocation as well as user association. The problem is then decomposed into three sub-problems and solved successively, where RSSI (received signal strength indicator) as the standard determines the user association, while the algorithms of multi-stage matching and successive convex approximation are used for solving spectrum allocation and power allocation, respectively. Simulation results demonstrate that the proposed algorithm can effectively increase the total capacity of the uplink coexisting networks.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040959
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 9
    Online Resource
    Online Resource
    Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-15), p. 960-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-15), p. 960-
    Abstract: In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the constraint of limited computing resources. The method utilizes deep reinforcement learning for the selection of optimal edge servers for task deployment, followed by a greedy self-adaptive stochastic algorithm for the recruitment of sensing participants. In simulations, the proposed method demonstrated a 20% improvement in spatial coverage compared with the existing RBR algorithm and outperformed the LCBPA, SMA, and MOTA algorithms in 41, 42, and 48 tasks, respectively. This research contributes to the optimization of task allocation in MCS and advances the integration of edge computing in MCS systems.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040960
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    Wissenschaftspark Albert Einstein
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    FH Potsdam
  • 10
    Online Resource
    Online Resource
    Performance Improvement of DTC-SVM of PMSM with Compensation for the Dead Time Effect and Power Switch Loss Based on Extended Kalman Filter
    MDPI AG ; 2023
    In:  Electronics Vol. 12, No. 4 ( 2023-02-15), p. 966-
    Details
    In: Electronics, MDPI AG, Vol. 12, No. 4 ( 2023-02-15), p. 966-
    Abstract: Two algorithms have been extensively studied for motor control: Field Oriented Control (FOC) and Direct Torque Control (DTC). Both control algorithms use a Voltage Source Inverter (VSI) to drive a Permanent Magnet Synchronous Motor (PMSM). To prevent short-arm short-circuit accidents when driving PMSM using VSI, a dead time is used to turn off the TOP and BOTTOM switches of each arm at the same time. However, this dead-time technique causes an unexpected pole voltage to be applied to the PMSM on the VSI output voltage, causing distortion and resulting in control nonlinearity. The disturbance voltage that causes nonlinearity is difficult to measure directly with the sensor. Therefore, this paper analyzes the nonlinearity of the controller due to the distorted voltage caused by the dead time during PMSM operation using the DTC algorithm and predicts the distorted output voltage using the extended Kalman Filter (EKF) to improve control stability. As a result, The algorithm proposed in this paper has verified the improvement of torque ripple and stator flux ripple through experiments and simulations.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics12040966
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662127-7
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    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
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