• Journal of Information Processing Systems
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• v.15 no.4
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• pp.833-852
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• 2019

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

• Tribology and Lubricants
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• v.36 no.6
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• pp.307-314
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• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• Journal of English Language & Literature
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• v.56 no.3
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• pp.519-541
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• 2010

A number of recent Victorian studies have participated in a renewed focus on form. E. Warwick Slinn and Monique R. Morgan, for instance, have contributed to enhancing our understanding of the Victorian dramatic monologue. This paper aims to expand what they have addressed by revisiting the mechanics of the dramatic form as a form, in particular addressing two types of dramatic monologue represented with supreme adroitness by Robert Browning and Alfred, Lord Tennyson, both of whom successfully attempted to widen our epistemology through a large act of the poetic imagination and great intellectual power. To this end, this paper lays particular attention to the role of the reader who is regarded as a key element of the dramatic aspect of the genre. In the dramatic monologue proper, real readers are actively brought into dialogic relation with the speaker or the poet, or both, whereby it seeks to represent an act of play among the poet, the speaker, and the reader. What the genre achieves in this fashion is twofold. For one thing, it pushes itself sufficiently to the very centre of the complex of apparently various narrative motives that animate the genre; for another, it honours the world of multiple viewpoints more than any other previous form of literature, all the more so as readers' views vary across their own time, space, and socio-cultural contexts. Incidentally, in one way and another, the dramatic monologue is of kinship with a Jamesian type of fiction, which is noted for its exterior impersonality. So this paper concludes by suggesting some theoretical implications that the dramatic genre assumes for, not only the naturalist novel, but also the (post-)modernist one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• The Plant Pathology Journal
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• v.40 no.1
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• pp.83-97
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• 2024

Fusarium graminearum, the causal agent of Fusarium head blight (FHB) in cereal crops, employs the production of sexual fruiting bodies (perithecia) on plant debris as a strategy for overwintering and dissemination. In an artificial condition (e.g., carrot agar medium), the F. graminearum Z3643 strain was capable of producing perithecia predominantly in the central region of the fungal culture where aerial hyphae naturally collapsed. To unravel the intricate relationship between natural aerial hyphae collapse and sexual development in this fungus, we focused on 699 genes differentially expressed during aerial hyphae collapse, with 26 selected for further analysis. Targeted gene deletion and quantitative real-time PCR analyses elucidated the functions of specific genes during natural aerial hyphae collapse and perithecium formation. Furthermore, comparative gene expression analyses between natural collapse and artificial removal conditions reveal distinct temporal profiles, with the latter inducing a more rapid and pronounced response, particularly in MAT gene expression. Notably, FGSG_09210 and FGSG_09896 play crucial roles in sexual development and aerial hyphae growth, respectively. Taken together, it is plausible that if aerial hyphae collapse occurs on plant debris, it may serve as a physical cue for inducing perithecium formation in crop fields, representing a survival strategy for F. graminearum during winter. Insights into the molecular mechanisms underlying aerial hyphae collapse provides offer potential strategies for disease control against FHB caused by F. graminearum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1301-1316
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• 2024

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

• Journal of Internet Technology
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• v.21 no.5
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• pp.1363-1374
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• 2020

Contents services have been provided to people in a variety of ways. Jukebox service is one of the contents streaming which provides an automated music-playing service. User inserts coin and presses a play button, the jukebox automatically selects and plays the record. The Disk Jockey (DJ) in Korean cafeteria (café) received contents desired of customer and played them through the speakers in the store. In this paper, we propose a service platform that reinvented the Korean café DJ in an integrated environment of IoT and cloud computing. The user in a store can request contents (music, video, and message) through the service platform. The contents are provided through the public screen and speaker in the store where the user is located. This allows people in the same location store to enjoy the contents together. The user information and the usage history are collected and managed in the cloud. Therefore, users can receive customized services regardless of stores. We compare our platform to exist services. As a result of the performance evaluation, the proposed platform shows that contents can be efficiently provided to users and adapts IoT-Cloud integrated environments.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.182-190
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• 2007

Virtual world is the 3D graphical interactive environment that networked by electronic communications. Virtual Worlds offered flow experience for a long time to their users. They blur the boundaries of work and play, so bring out the concept of Fun Labor. If we can accept the principle of equivalence between Fun Labor and Real Labor, the Fun Labor may be one of the solutions of the large unemployment problem in the information society. And the Fun Labor is the new type of labor that corresponds the subjectivity of users who want interesting experience as much as they spend money and times. This situation means that the users of Virtual worlds are structuring the identities as the Residents who act the Fun Labor. It'll be very important to examine the social effects of this situation.