Bulletin of Electrical Engineering and Informatics
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CaO:Tb3+ green-emitting phosphor for white light-emitted diode-phosphor applications: the improvement of light output intensity
Get PDFThe use of CaO:Tb3+ with light green emission on for improvement of both luminescent output and chromatic fidelity of the white light emitted from a light-emitting diode (LED). The CaO:Tb3+ is combined with the yellow-emitting phosphor of YAG:Ce3+ to provide sufficient colored spectral proportion for the white light generation, enhancing the color performance. The phosphor combination is utilized for the three most applied LED structures: conformal, in-cup, and remote phosphor structures. The changes in optical properties of these three LEDs are monitored with adjustments in the proportion of CaO:Tb3+. The higher proportion of the green phosphor results in higher scattering efficiency in all structures, offering better color coordination and stronger luminous flux. The color quality scale is somehow reduced when CaO:Tb3+ concentration is more than a certain level. Therefore, depending on the phosphor configuration of the white light-emitting diode (WLED), the concentration of CaO:Tb3+ should be modified to achieve a good color rendition with improved color consistency and luminous properties
Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon
Get PDFDisease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant
Recent trend and future prospect in optimization of electric vehicle charging: a systematic review
Get PDFElectric vehicles charging (EVs) must be done optimally to minimize the impact it causes. EVs are being recognized as a potential way to decrease greenhouse gas emissions and combat climate change. However, there are still difficulties in optimizing these systems to minimize operating costs and EVs charging waiting times. This study investigates several industrial, commercial and residential charging stations. The primary objective of this study is to systematically review the existing literature on optimizing EV charging. The collection of data was centered on scholarly articles released between the years 2018 and 2023 from Scopus, IEEE Xplore. This study presents a systematic literature review of optimizing EVs charging. As a result, 43 EVs charging optimization studies were obtained which were investigated and studied further. Identify and analysis the selected studies, there are two research topics and trends most frequently addressed by researchers: scheduling and coordination. The four most applied methods in EVs charging are identified: particle swarm optimization (PSO), genetic algorithm (GA), linear programming (LP) method, and evolutionary algorithms (EA). Future research directions: develop advanced optimization algorithms, investigating the integration of renewable energy sources into the charging infrastructure, exploring the potential of vehicle-to-grid (V2G) services, studying the impact of EVs charging on the power grid and developing strategies, considering the optimization of charging schedules and coordination strategies for large-scale EVs fleets
Exploiting channel state information of WiFi signal for human activity detection: an experimental study
Get PDFUbiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications
Feature importance for software development effort estimation using multi level ensemble approaches
Get PDFFeature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models
Improved car detection performance on highways based on YOLOv8
Get PDFCar detection on the road through computer vision is crucial for improving safety, as it plays an essential role in spotting nearby vehicles and preventing fatal accidents. Additionally, car detection significantly contributes to the advancement of autonomous vehicles. Previous explorations of car detection using YOLOv5 have revealed weaknesses regarding its resulting mean average precision (mAP). This scenario led to the development of a more advanced version of you only look once (YOLO), namely YOLOv8. Consequently, this study aimed to adopt YOLOv8 for automatic car detection on the road. YOLOv8 is proven to perform better than the previous version. A dataset comprising video frame images was captured on the highway in Semarang, Indonesia. The experiment results indicated that the proposed approach achieved impressive precision, recall, and mAP values, reaching 94.1%, 98.2%, and 98.8%, respectively. The proposed approach enhanced mAP and training time when compared with YOLOv5. Therefore, it was concluded that the proposed method was better suited for real-time car detection
Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system
Get PDFFeature selection (FS) plays an important role in the construction of efficient ensemble classifiers; particularly for intrusion detection system (IDS). An IDS is a utilized in a network architecture to protect the availability of sensitive information. However, existing IDSs suffer from redundancy, high dimensionality, and high false alarm rate (FAR). Also, lots of models are constructed for outdated datasets, which makes them less flexible to deal with new assaults. Therefore, this paper proposes a new IDS relies on hybrid FS and ensemble classifiers. A hybrid FS approach consists of two techniques, hard-voting and mean. In contrast to recent papers, we use three different FS approaches: extra tree classifier importance as an embedded FS, recursive feature elimination (RFE) as a wrapper FS, and mutual information (MI) as a filter FS. Then, a hard-voting technique has been used to fuse output of these approaches and obtain a reduced subset of features. Since each feature has three weights, a mean technique has been utilized to assign one weight to each feature and obtain an optimal subset of features. The experimental outcomes, utilizing the modern InSDN dataset, confirm that the proposed hybrid FS with ensemble soft voting classifier achieves better results than other ensemble and individual classifiers due to several measures
Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach
Get PDFThis paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory
Comparison of 5G performance post-merger between two network operators using field tests in urban areas
Get PDFIn late Q1/2023, DTAC and TRUE officially completed their merger. Consequently, this study was initiated to ascertain whether their respective fifth-generation (5G) networks had been seamlessly integrated several months following the merger. The investigation involved conducting drive tests along two predefined routes within the urban areas of Bangkok, employing the G-NetTrack pro tool for testing and data collection. Additionally, stationary tests were conducted in two crowded places using an application called Speedtest. Subsequently, an array of quality of service (QoS) metrics, including reference signal received power (RSRP), reference signal received quality (RSRQ), signal to noise ratio (SNR), download (DL), upload (UL) speeds and latency, were meticulously analyzed and presented. The findings of this study unveiled that, despite the successful completion of the DTAC and TRUE merger from a business standpoint, the technical integration of their respective 5G networks had not been finalized, although there were no significant differences between DTAC and TRUE for DL (p-value=0.542) and UL (p-value=0.090). Notably, significant differences were found between DTAC and TRUE for four metrics, including RSRP, RSRQ, SNR and latency (p-values0.05). Remarkably, roaming functionalities were still operational between the two networks
An enhanced control scheme for multifunctional grid connected PV system using fuzzy and predictive direct power control
Get PDFThis paper presents a combination between a fuzzy logic control (FLC) and a predictive direct power control for multifunctional grid connected photovoltaic (PV) system, to solve the oscillation problem in the DC link voltage of the in-verter caused by the fast irradiation changing. The whole system consists of a PV system which interface a DC-AC inverter, a FLC maximum power point tracking (MPPT) algorithm has been adopted to operate the DC-DC converter at the MPP. The predictive control strategy is applied to the DC-AC inverter with FLC in its voltage control loop to improve the power exchange between the grid and the PV system. Simulation results have been verified through MATLAB/Simulink software for the purpose of giving the effectiveness of the suggested control against existed controllers