Vol.21 No.3, September 30, 2023
Abstract : The Internet of Things (IoT) can be defined as the connection of devices, sensors, and actors via the Internet to a single network to provide services to end-users. Owing to the flexibility and simplicity of IoT devices, which impart convenience to end-users, the demand for these devices has increased significantly in the last decade. To make these systems more scalable, achieve a larger number of connected devices, and achieve greater economic success, it is vital to develop them by considering parameters such as security, cost, bandwidth, data rate, and power consumption. This study aims to improve energy efficiency and prolong the lifetime of IoT networks by proposing a new approach called the constrained application protocol CoAP45. This approach reduces the number of updates to the CoAP server using a centralized resource. The simulation results show that the proposed approach outperforms all existing protocols.
Abstract : A Distributed reflection denial of service (DRDoS) is a variant of DDoS attacks that threatens the availability of services to legitimate users. In response to this evolving threat landscape, the cybersecurity industry and service providers have intensified their efforts to develop effective countermeasures. Despite these efforts, attackers continue to innovate, developing new strategies and tools while becoming more sophisticated. Consequently, DRDoS attacks continue to be harmful. Therefore, ongoing research and development is essential to improve defense against DRDoS attacks. To advance our understanding and analysis of DRDoS attacks, this study examines the unique characteristics of DRDoS attacks and quantifies the risks involved. Additionally, it adopts a quantitative rather than traditional qualitative methods to derive and apply risk, particularly the probability of loss that can be caused by DRDoS attacks.
Abstract : Responding to changes in artificial intelligence models and the data environment is crucial for increasing data-learning accuracy and inference stability of industrial applications. A learning model that is overfitted to specific training data leads to poor learning performance and a deterioration in flexibility. Therefore, an early stopping technique is used to stop learning at an appropriate time. However, this technique does not consider the homogeneity and independence of the data collected by heterogeneous nodes in a differential network environment, thus resulting in low learning accuracy and degradation of system performance. In this study, the generalization performance of neural networks is maximized, whereas the effect of the homogeneity of datasets is minimized by achieving an accuracy of 99.7%. This corresponds to a decrease in delay time by a factor of 2.33 and improvement in performance by a factor of 2.5 compared with the conventional method.
Abstract : Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.
Abstract : Smart lighting systems have become increasingly popular in several public sectors because of trends toward urbanization and intelligent technologies. In this study, we designed and implemented a web application platform to explore and monitor data acquired from lighting devices at Thammasat University (Rangsit Campus, Thailand). The platform provides a convenient interface for administrative and operative staff to monitor, control, and collect data from sensors installed on campus in real time for creating geographically specific big data. Platform development focuses on both back- and front-end applications to allow a seamless process for recording and displaying data from interconnected devices. Responsible persons can interact with devices and acquire data effortlessly, minimizing workforce and human error. The collected data were analyzed using an exploratory data analysis process. Missing data behavior caused by system outages was also investigated.
Abstract : Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson’s heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.
Abstract : This study presents a method for identifying partial discharge defects in an eco-friendly gas insulated system using a back-propagation algorithm. Four partial discharge (PD) electrode systems, namely, a free-moving particle, protrusion on the conductor, protrusion on the enclosure, and voids, were designed to simulate PD defects that can occur during the operation of eco-friendly gas-insulated switchgear. The PD signals were measured using an ultrahigh-frequency sensor as a nonconventional method based on IEC 62478. To identify the types of PD defects, the PD parameters of single PD pulses in the time and frequency domains and the phase-resolved partial discharge patterns were extracted, and a back-propagation algorithm in the artificial neural network was designed using a virtual instrument based on LabVIEW. The backpropagation algorithm proposed in this paper has an accuracy rate of over 90% for identifying the types of PD defects, and the result is expected to be used as a reference database for asset management and maintenance work for eco-friendly gas-insulated power equipment.
Abstract : Owing to the environment in which it has become difficult to face people, on-tact communication has been activated, and online video conferencing platforms have become indispensable collaboration tools. Although costs have dropped and productivity has improved, communication remains poor. Recently, various companies, including existing online videoconferencing companies, have attempted to solve communication problems by establishing a videoconferencing platform within the virtual reality (Virtual Reality) space. Although the VR videoconference platform has only improved upon the benefits of existing video conferences, the problem of manually summarizing minutes because there is no function to summarize minute documents still remains. Therefore, this study proposes a method for establishing a meeting minute summary system without applying cases to a VR videoconference platform. This study aims to solve the problem of communication difficulties by combining VR, a metaverse technology, with an existing online video conferencing platform.
Abstract : Engineering or humanities data are stored in databases and are often used for search services. While the latest deep-learning technologies, such like BART and BERT, are utilized for data analysis, humanities data still rely on traditional databases. Representative analysis methods include n-gram and lexical statistical extraction. However, when using a database, performance limitation is often imposed on the result calculations. This study presents an experimental process using MariaDB on a PC, which is easily accessible in a laboratory, to analyze the impact of the database on data analysis performance. The findings highlight the fact that the database becomes a bottleneck when analyzing large-scale text data, particularly over hundreds of thousands of records. To address this issue, a method was proposed to provide real-time humanities data analysis web services by leveraging the open source database, with a focus on the Seungjeongwon-Ilgy, one of the largest datasets in the humanities fields.
Abstract : A bullet launcher can be developed as a smart instrument, especially for use in the military section, that can track, identify, detect, mark, lock, and shoot a target by implementing an image-processing system. In this research, the application of object recognition system, laser encoding as a unique marker, 2-dimensional movement, and pneumatic as a shooter has been studied intensively. The results showed that object recognition system could detect various colors, patterns, sizes, and laser blinking. Measuring the average error value of the object distance by using the camera is ±4, ±5, and ±6% for circle, square and triangle form respectively. Meanwhile, the average accuracy of shots on objects is 95.24% and 85.71% in indoor and outdoor conditions respectively. Here, the average prototype response time is 1.11 s. Moreover, the highest accuracy rate of shooting results at 50 cm was obtained 98.32%.
Ti-Hon Nguyen and Thanh-Nghi Do, Member, KIICEJournal of information and communication convergence engineering 2022;20: 309-316 https://doi.org/10.56977/jicce.2022.20.4.309
Ashraf Al Sharah, Hamza Abu Owida, Talal A. Edwan, and Feras AlnaimatJournal of information and communication convergence engineering 2022;20: 250-258 https://doi.org/10.56977/jicce.2022.20.4.250