2022 International Conference on Future Trends in Smart Communities (ICFTSC)最新文献_第3页

Design of an Efficient LED Driver using Non-Ideal Boost Converter for Lighting Applications 基于非理想升压变换器的高效LED驱动设计

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040070

K. Sundari, K. Renganathan, R. Azhagumurugan, R. Kannan, A. Jeyadharshini, P. Mahalakshmi

{"title":"Design of an Efficient LED Driver using Non-Ideal Boost Converter for Lighting Applications","authors":"K. Sundari, K. Renganathan, R. Azhagumurugan, R. Kannan, A. Jeyadharshini, P. Mahalakshmi","doi":"10.1109/ICFTSC57269.2022.10040070","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040070","url":null,"abstract":"Design and enhancement of an efficient lighting system is the pressing priority for ensuring Sustainable Development Goa1-7 Affordable and clean energy. Solar energy has been preferred as a source since it is clean energy source with net - zero carbon and has incredible potential energy which can be captured using a diverse range of easily accessible equipment for residential and commercial applications, with the added benefit of low hassle and zero operational cost. Despite the fact that solar energy is abundant throughout the world, the tracking of power towards its optimal operation PV systems is a critical component of achieving higher conversion. The MPPT method based on incremental conductance (IC) is used here. In this work, energy to light up an Light Emitting Diode (LED) is obtained from a 40 Watts, 12 Volts Solar Panel. This voltage is amplified by implementing a DC/DC Boost converter. After obtaining a reference voltage from the MPPT, the set point for the current is reaped from a brightness control circuit which receives input from a light detecting sensor. The DC/DC Boost Converter considered here is modelled using small signal modelling technique by accounting for non-idealities produced by inductors’ and capacitors’ electro-resistance (ESR) and voltage variations created by the presence of a diode. The challenges brought on by the DC/DC Boost converter’s right half plane zero are construeed, and a proportional plus integral controller is suggested to alleviate the associated control constraints. Using MATLAB simulation studies, the photometric parameters were computed from the observed values to appraise the dominance of the LED driver. Power calculations are performed to prove the proposed converter’s effectiveness. Comparison among IMC approach and Roberto method is done and it is inferred that Roberto method is proven to be effective.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132680685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Energy Management Algorithm in Wireless Sensor Network for Pipeline Monitoring 用于管道监测的无线传感器网络能量管理算法

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039825

L. S. Ayinla, A. A. Aziz, M. Drieberg, Austin C. O. Azubogu, T. I. Amosa

{"title":"Energy Management Algorithm in Wireless Sensor Network for Pipeline Monitoring","authors":"L. S. Ayinla, A. A. Aziz, M. Drieberg, Austin C. O. Azubogu, T. I. Amosa","doi":"10.1109/ICFTSC57269.2022.10039825","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10039825","url":null,"abstract":"Energy consumption modelling is the starting point for the development and assessment of accurate Energy Management (EM) schemes in Wireless Sensor Network (WSN). Energy consumption is the greatest obstacle to the widespread deployment of WSNs. EM is crucial for remotely deployed sensor nodes with energy constraints. As energy is considered a finite resource for sensor nodes, an effective EM scheme is essential for managing the energy resource. In this paper, an energy consumption model and duty cycle-based EM algorithm for acceleration based WSN are proposed. On a 21 mx1Sm outdoor experimental testbed of acceleration based WSN, this algorithm was tested and evaluated. The testbed was configured with six valves labelled V1 through V6. During the experiments, we manually adjusted the valves to three different states, namely fully closed (FC), half-opened (HO), and fully opened (FO), to simulate rupture on the pipeline network. The simulated events demonstrated that acceleration amplitude (g-force) is proportional to rupture size and rupture distance from the sensor node location. These measured values were used to determine the magnitude and location of the pipeline network rupture. Comparing the established EM algorithm to the network operating continuously without the algorithm, nearly 95% of the energy was conserved.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132250349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable Deep Learning Model for Cardiac Arrhythmia Classification 心律失常分类的可解释深度学习模型

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039860

Talal A. A. Abdullah, Mohd Zahid, T. Tang, Waleed Ali, Maged Nasser

{"title":"Explainable Deep Learning Model for Cardiac Arrhythmia Classification","authors":"Talal A. A. Abdullah, Mohd Zahid, T. Tang, Waleed Ali, Maged Nasser","doi":"10.1109/ICFTSC57269.2022.10039860","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10039860","url":null,"abstract":"In this work, we proposed a hybrid deep learning model that (CNN-GRU) combines a One-Dimensional Neural Network (1D CNN) and a Gated Recurrent Unit (GRU) to classify four types of cardiac arrhythmia and applied LIME to provide explanations for its predictions. LIME is a well-known local explanation method that can explain any machine learning model by simulating its behaviours to generate explanations. However, LIME can only explain tabular, text, and image datasets. Therefore, we proposed a visual presentation of LIME on signal dataset by applying a heatmap to highlight important areas on the heartbeat signals. Moreover, we propose an effective method to segment heartbeats from ECG records, ensuring that all key features are extracted correctly, such as QRS Complex, P Wave, and T Wave. The proposed hybrid model was trained using ECG lead II from the MIT-BIH dataset and evaluated based on accuracy, precision, recall, f1 score, and AUC-ROC performance matrix. To highlight the proposed model’s validity, we compare it against the standalone CNN and GRU models and prove its superiority in terms of accuracy and ROC.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124212287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Comparative Analysis of Different Parameters used for Optimization in the Process of Speaker and Speech Recognition using Deep Neural Network 基于深度神经网络的说话人和语音识别过程中不同优化参数的比较分析

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040065

S. Natarajan, S. Al-Haddad, Faisul Arif Ahmad, Mohd Khair Hassan, Raja Kamil, S. Azrad, Mohammed Nawfal Yahya, June Francis Macleans, Pratiksha Prashant Salvekar

{"title":"Comparative Analysis of Different Parameters used for Optimization in the Process of Speaker and Speech Recognition using Deep Neural Network","authors":"S. Natarajan, S. Al-Haddad, Faisul Arif Ahmad, Mohd Khair Hassan, Raja Kamil, S. Azrad, Mohammed Nawfal Yahya, June Francis Macleans, Pratiksha Prashant Salvekar","doi":"10.1109/ICFTSC57269.2022.10040065","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040065","url":null,"abstract":"The process of speaker recognition in a noisy and distant environment is a difficult task as it faces numerous challenges before clean speaker speech signal reaching the microphone. While developing a deep neural network for robust functioning in extreme conditions, the selection of a perfectly compatible optimizer, loss function, and dropout is necessary. This paper presents a comparative study of the optimization process in the neural network, how loss function effectively unites in seeking the optimizer. It emphasizes on the selection of the number of input nodes, hidden layers, and time consumed by each set of selections. This study elaborates the accuracy obtained at different combinations of parameters for robust deep neural network structure. This paper is classified under speaker and speech recognition process into improving accuracy. Experiment results shows that Adam optimizer with 150 epochs offers around 95% accuracy for speaker classification under the noisy condition at different SNR values.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122719426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting of Wind Turbines Generated Power With Missing Input Variables 缺少输入变量的风力发电功率预测

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039884

M. Sunder, R. Abishek, Monalisa Maiti, Kishore Bingi, P. Devan, M. Assaad

{"title":"Forecasting of Wind Turbines Generated Power With Missing Input Variables","authors":"M. Sunder, R. Abishek, Monalisa Maiti, Kishore Bingi, P. Devan, M. Assaad","doi":"10.1109/ICFTSC57269.2022.10039884","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10039884","url":null,"abstract":"The power generated by electric wind turbines undergoes rapid changes due to continuous fluctuation of wind speed, direction, atmospheric pressure, etc. Providing the power industry with the capability to estimate these performance characteristics helps in the pre-planning of maintenance, which helps in power management by assessing the generated power for the day. However, forecasting the generated power with any missing input parameters is quite challenging. Therefore, this paper proposes a forecasting model with three types of neural networks to handle one missing input parameter to predict the wind turbine’s generated power. Firstly, a Feed Forward Neural Network (FFNN) is developed to forecast generated power from all four available input parameters. Later the FFNN, along with a Long Short-Term Memory (LSTM) and Nonlinear Autoregressive (NAR) neural networks, are modeled to handle the missing input parameter. The main FFNN then uses the predicted parameter to forecast the generated power. The results from the simulation study have indicated that the proposed strategy achieved the best performance in predicting the missing input and the system’s generated power.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127296741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mathematical Modelling & Design Analysis of Pipeline Vibration-based Piezoelectric Energy Harvester 基于管道振动的压电能量采集器的数学建模与设计分析

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039869

W. N. Mohd Fairuz, M. R. Ahmad, I. Nawi

{"title":"Mathematical Modelling & Design Analysis of Pipeline Vibration-based Piezoelectric Energy Harvester","authors":"W. N. Mohd Fairuz, M. R. Ahmad, I. Nawi","doi":"10.1109/ICFTSC57269.2022.10039869","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10039869","url":null,"abstract":"Pipeline exhibits turbulence-induced vibrations while conveying fluids or gas. However, the pipeline vibrations are less likely to cause structural failure as it exists in a small magnitude and can be harvested into useful energy. This paper presents research on the piezoelectric energy harvesting method utilizing mechanical energy from pipeline vibration into electrical energy to power up the pipeline monitoring sensor. This study focuses on the development and evaluation of a Piezoelectric Energy Harvester (PEH) module, which will theoretically improve output power for low-powered device applications. Mathematical modelling of the Piezoelectric Energy Harvester was constructed using MATLAB/Simulink whereas the Finite Element Modelling (FEM) analysis is carried out using COMSOL Multiphysics software. Characterization of the design using One Degree of Freedom (IDOF) Piezoelectric Energy Harvester was carried out as a reference to optimize the parameters to be used for the analysis of multiple designs. The performances of 13 different designs of Piezoelectric Energy Harvester were observed to check the design that produces the highest output voltage and output power within the safe vibration region of the pipeline which is from 10 Hz to 300 Hz. From the results obtained, One Degree of Freedom Piezoelectric Energy Harvester was able to produce 17.5761 V and 4.9826 mW at a resonant frequency of 19S.S6 Hz. Meanwhile, Serpentine with Two-Anchor Piezoelectric Energy Harvester yielded up to 40.9229 V and 27.0099 mW at a resonant frequency of 119.67 Hz. The simulation proved that the Serpentine Vibration-Based Piezoelectric Energy Harvester has the potential of producing the expected output power for pipeline monitoring applications.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131358184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity Analysis of the Effective Reproduction Number in a Sarawak Rabies Epidemic Model 沙捞越狂犬病流行模型中有效繁殖数的敏感性分析

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039940

Nur Asheila Abdul Taib, J. Labadin

引用次数: 0

mm-Wave RSS Evaluation for Distance Estimation in Urban Environments 城市环境中距离估计的毫米波RSS评价

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040059

Yaser Bakhuraisa, A. B. Abd.Aziz, T. K. Geok, Norazhar B. Abu Bakar, S. Jamian

{"title":"mm-Wave RSS Evaluation for Distance Estimation in Urban Environments","authors":"Yaser Bakhuraisa, A. B. Abd.Aziz, T. K. Geok, Norazhar B. Abu Bakar, S. Jamian","doi":"10.1109/ICFTSC57269.2022.10040059","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040059","url":null,"abstract":"In the recent years, mm-wave bands have become popular in the modern wireless communication and vehicular positioning. However, accurate estimation of the distance between the base station and the vehicle is very important to improve localization accuracy. In this work, we evaluated the accuracy of the distance estimation based on the received signal strength (RSS) model for 28 GHz mm-wave in urban environments with LOS and NLOS scenarios. Ray tracing method have been used to predict the RSS of the aforementioned frequency band. The parameters of path loss model, i.e., Close In Log-Distance (CILD) Model, are derived based on linear regression of predicted RSS. The results showed that, RSS model have provided an acceptable level of distance estimation. It provided more accurate estimation in the LOS scenario compared to NLOS scenario. The correlation coefficients (R2) between the actual distance and the estimated distance were 0.76 and 0.73 for LOS and NOLS scenarios respectively. The mean absolute error for distance estimation was 3. S23m in LOS, while 4. SS7m was obtained for NLOS.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Offloading Decision and Resource Allocation for Mobile Edge Computing 移动边缘计算智能卸载决策与资源分配

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10039944

Omar Baslaim, A. Awang

{"title":"Intelligent Offloading Decision and Resource Allocation for Mobile Edge Computing","authors":"Omar Baslaim, A. Awang","doi":"10.1109/ICFTSC57269.2022.10039944","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10039944","url":null,"abstract":"Mobile Edge Computing (MEC) is one of the most promising paradigms for overcoming Edge Devices (EDs) constraints. These EDs suffer from resource limitations in terms of power and computation.MEC will be more prevalent with the rising resource- intensive and time-sensitive EDs applications. MEC is considered a superior alternative to cloud computing. Despite computational offloading to the cloud offeringsignificant benefits related to computing and storage, EDs are geographically distant from the cloud, leading to significant transmission delays. However, offloading to the nearest server and ignoring the huge capabilities of the cloud is not always a good option. In contrast, local computing is rarely preferable. On the other hand, sometimes offloading to the nearest server is impossible, because of the current state of the server. These possibilities, as well as MEC system unpredictability, make the offloading decision difficult and critical. Therefore, the idea of the proposed model is based on Reinforcement Learning (RL). Moreover, the model is designed to make an optimal decision amongthe three offloading options; nearest edge server, best edge server, and cloud. The edge server can decide to offload tasks to the optimal available edge server or cloud directly, which depends on several parameters for reducing execution time and energy consumption. In addition, the edge server connects to all componentswithin its region, which improve the managing of resource allocation. This proposed model is expected to be optimal in edge servers connection and intelligent offloading decisions.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127872079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improved Whale Optimization Algorithm for Optimal Network Coverage in Industrial Wireless Sensor Networks 工业无线传感器网络最优网络覆盖的改进Whale优化算法

2022 International Conference on Future Trends in Smart Communities (ICFTSC) Pub Date : 2022-12-01 DOI: 10.1109/ICFTSC57269.2022.10040067

P. Devan, R. Ibrahim, M. Omar, Kishore Bingi, H. Abdulrab, F. Hussin

{"title":"Improved Whale Optimization Algorithm for Optimal Network Coverage in Industrial Wireless Sensor Networks","authors":"P. Devan, R. Ibrahim, M. Omar, Kishore Bingi, H. Abdulrab, F. Hussin","doi":"10.1109/ICFTSC57269.2022.10040067","DOIUrl":"https://doi.org/10.1109/ICFTSC57269.2022.10040067","url":null,"abstract":"This paper aims to develop an improved whale optimization algorithm (IWOA) using naturally occurring spiral characteristics for optimal router placement in the industrial wireless sensor networks (IWSN) with adequate network connectivity and coverage for all the available clients in its network. The proposed algorithm uses the widely known Archimedean spiral characteristics for the humpback whale’s bubble-net hunting behaviour. The proposed algorithm is compared with the existing whale optimization algorithm (WOA) over multiple optimization benchmark test functions using various spiral patterns. Furthermore, the algorithm is additionally validated for the IWSN problem to obtain the optimal locations for placing the routers in the network to provide maximum connectivity and client coverage for all the available clients with possible minimization of the area overlapping. The numerical and convergence results show that using sin and cos based spiral behaviours improved the convergence rate by 42.866% and 100% in benchmark test functions and IWSN problem, respectively.","PeriodicalId":386462,"journal":{"name":"2022 International Conference on Future Trends in Smart Communities (ICFTSC)","volume":"80 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125886099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2