Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231210
Ahmad Massud Tota Khel, Xiaohong Peng
In this paper, a four-element MIMO antenna with slots and partial ground plane for 5G communication systems operating at C-band (3.4GHz - 3.8GHz) is presented. It is designed on Rogers RT/Duriod 5880 with a dielectric constant and thickness of 2.2 and 0.787mm, respectively. The dimensions of the slots and the partial ground plane are optimized by applying a heuristic search algorithm, the Hill Climbing, to increase the impedance bandwidth and reduce the mutual coupling between the antenna elements without using a decoupling structure or extra spacing. It achieves a huge bandwidth of 539.4MHz covering 3.2926GHz to 3.832GHz at S11 ≤ -10dB and resonates at 3.595GHz with a reflection coefficient and mutual coupling of -46.45dB and < -16dB, respectively. It also achieves an envelope correlation coefficient (ECC) of < 0.017, diversity gain (DG) of ~10dB and channel capacity loss (CCL) of < 0.18bits/sec/Hz throughout the operating frequency band, respectively.
{"title":"A Slotted MIMO Antenna for 5G C-band Communication Systems","authors":"Ahmad Massud Tota Khel, Xiaohong Peng","doi":"10.1109/iCCECE49321.2020.9231210","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231210","url":null,"abstract":"In this paper, a four-element MIMO antenna with slots and partial ground plane for 5G communication systems operating at C-band (3.4GHz - 3.8GHz) is presented. It is designed on Rogers RT/Duriod 5880 with a dielectric constant and thickness of 2.2 and 0.787mm, respectively. The dimensions of the slots and the partial ground plane are optimized by applying a heuristic search algorithm, the Hill Climbing, to increase the impedance bandwidth and reduce the mutual coupling between the antenna elements without using a decoupling structure or extra spacing. It achieves a huge bandwidth of 539.4MHz covering 3.2926GHz to 3.832GHz at S11 ≤ -10dB and resonates at 3.595GHz with a reflection coefficient and mutual coupling of -46.45dB and < -16dB, respectively. It also achieves an envelope correlation coefficient (ECC) of < 0.017, diversity gain (DG) of ~10dB and channel capacity loss (CCL) of < 0.18bits/sec/Hz throughout the operating frequency band, respectively.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126170440","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231063
J. Roscoe, Oliver Baxandall, R. Hercock
Connected and autonomous vehicles (CAVs) are an emerging technology that will introduce new threats to the general public. Impending standards (such as ISO21434) demonstrate that there is a real cyber security risk and a need for supporting infrastructure in the form of vehicle security operations centre.In this concept paper we discuss some of the issues facing vehicle security as the technology matures over the next few years and look at how epidemiological models for malware might be developed to address concerns over vehicle cyber threats.We detail our development of Mobius, a bespoke tool for simulating and analysing malware events in CAVs and explore how the technology might be applied to support real-world decision making.As a part of the need for cyber resilience, we suggest there is a key role for vehicle simulation software capable of modelling cyber threats to assist with threat analysis and decision making for highway authorities, OEMs and fleet operators, amongst others. We present a summary of compartmental epidemiological models and the role they can play in understanding malware propagation for CAVs.
{"title":"Simulation of Malware Propagation and Effects in Connected and Autonomous Vehicles","authors":"J. Roscoe, Oliver Baxandall, R. Hercock","doi":"10.1109/iCCECE49321.2020.9231063","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231063","url":null,"abstract":"Connected and autonomous vehicles (CAVs) are an emerging technology that will introduce new threats to the general public. Impending standards (such as ISO21434) demonstrate that there is a real cyber security risk and a need for supporting infrastructure in the form of vehicle security operations centre.In this concept paper we discuss some of the issues facing vehicle security as the technology matures over the next few years and look at how epidemiological models for malware might be developed to address concerns over vehicle cyber threats.We detail our development of Mobius, a bespoke tool for simulating and analysing malware events in CAVs and explore how the technology might be applied to support real-world decision making.As a part of the need for cyber resilience, we suggest there is a key role for vehicle simulation software capable of modelling cyber threats to assist with threat analysis and decision making for highway authorities, OEMs and fleet operators, amongst others. We present a summary of compartmental epidemiological models and the role they can play in understanding malware propagation for CAVs.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124714510","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231158
Sunil Puranik, Mahesh Barve, Dhaval Shah, Sharad Sinha, R. Patrikar, Swapnil Rodi
Key-value Store (KVS) is one of the most important components in trading system for performing search operations. High Level Synthesis (HLS) provides a new flow for design of Field Programmable Gate Array (FPGA) systems. We describe a novel low latency, high throughput, memory efficient KVS block designed using conventional Verilog flow as well as High Level Synthesis flow and targeted to FPGA technology. We compare these two flows for designing KVS. Substantial advantage in gained in terms of productivity using HLS. The time for implementing in HLS is just 18% as compared to Verilog flow though the resource utilization in case of hand coded Verilog is better. The design shows promising performance numbers indicating that more complex FPGA systems could be designed using HLS.
{"title":"Key-Value Store using High Level Synthesis Flow for Securities Trading System","authors":"Sunil Puranik, Mahesh Barve, Dhaval Shah, Sharad Sinha, R. Patrikar, Swapnil Rodi","doi":"10.1109/iCCECE49321.2020.9231158","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231158","url":null,"abstract":"Key-value Store (KVS) is one of the most important components in trading system for performing search operations. High Level Synthesis (HLS) provides a new flow for design of Field Programmable Gate Array (FPGA) systems. We describe a novel low latency, high throughput, memory efficient KVS block designed using conventional Verilog flow as well as High Level Synthesis flow and targeted to FPGA technology. We compare these two flows for designing KVS. Substantial advantage in gained in terms of productivity using HLS. The time for implementing in HLS is just 18% as compared to Verilog flow though the resource utilization in case of hand coded Verilog is better. The design shows promising performance numbers indicating that more complex FPGA systems could be designed using HLS.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"3 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114090616","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231095
Will Serrano
The Random Neural Network with a Genetic algorithm and its integration into an Intelligent Building: iBuilding is proposed in this paper. The presented biological method, founded on the Genome, codifies and transmits the information from the Intelligent Building. Furthermore, it also multiplexes its data entirely to generate Clusters of Buildings that are interconnected with each other. The key concept proposed in this paper is that the learned information obtained by iBuilding after its interaction with the environment is never lost when the building is decommissioned or retrofitted but transmitted to future iBuilding generations as distributed organisms. Data is codified in the network weights instead of the neurons, similar as the Genome, in order to enable an Artificial Intelligence evolution in iBuilding. The presented biological algorithm is inserted into an iBuilding model where sensorial neurons distributed within the Intelligent Building collect measurements about its environment and select relevant information. This proposed model has been validated with several research datasets that cover several key scenarios; experimental results demonstrate that the Random Neural Network Genetic Algorithm codifies, transmits and multiplexes iBuilding information to future generations with insignificant error, therefore, successfully creating a cluster of buildings.
{"title":"The Random Neural Network with a Genetic algorithm in Intelligent Buildings","authors":"Will Serrano","doi":"10.1109/iCCECE49321.2020.9231095","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231095","url":null,"abstract":"The Random Neural Network with a Genetic algorithm and its integration into an Intelligent Building: iBuilding is proposed in this paper. The presented biological method, founded on the Genome, codifies and transmits the information from the Intelligent Building. Furthermore, it also multiplexes its data entirely to generate Clusters of Buildings that are interconnected with each other. The key concept proposed in this paper is that the learned information obtained by iBuilding after its interaction with the environment is never lost when the building is decommissioned or retrofitted but transmitted to future iBuilding generations as distributed organisms. Data is codified in the network weights instead of the neurons, similar as the Genome, in order to enable an Artificial Intelligence evolution in iBuilding. The presented biological algorithm is inserted into an iBuilding model where sensorial neurons distributed within the Intelligent Building collect measurements about its environment and select relevant information. This proposed model has been validated with several research datasets that cover several key scenarios; experimental results demonstrate that the Random Neural Network Genetic Algorithm codifies, transmits and multiplexes iBuilding information to future generations with insignificant error, therefore, successfully creating a cluster of buildings.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121900886","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231109
Takuro Hada, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga
In recent years, drug trafficking using microblogs has risen and become a social problem. A common method of cyber patrols for cracking down on crimes, such as drug trafficking, involves searching for crime-related keywords. However, criminals who post crime-inducing messages make maximum use of "codewords" rather than keywords, such as enjo kosai, marijuana, and methamphetamine, to camouflage their criminal intentions. Research suggests that these codewords change once they become popular; therefore, searching for a specific word requires significant effort to keep track of the latest codewords. In this study, we focused on the appearance of codewords and those likely to be included in incriminating posts with aim to detect codewords with the high likelihood of inclusion in incriminating posts. We proposed new methods for detecting codewords based on differences in word usage and conducted experiments on concealed-word detection in order to evaluate method effectiveness. The results showed that the proposed method was capable of detecting concealed words other than those in the initial list and to better degree relative to baseline methods. These findings demonstrated the ability of the proposed method to rapidly and automatically detect codewords that change over time and blog posts that induce crimes, thereby potentially reducing the burden of continuous monitoring of codewords.
{"title":"Codewords Detection in Microblogs Focusing on Differences in Word Use Between Two Corpora","authors":"Takuro Hada, Y. Sei, Yasuyuki Tahara, Akihiko Ohsuga","doi":"10.1109/iCCECE49321.2020.9231109","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231109","url":null,"abstract":"In recent years, drug trafficking using microblogs has risen and become a social problem. A common method of cyber patrols for cracking down on crimes, such as drug trafficking, involves searching for crime-related keywords. However, criminals who post crime-inducing messages make maximum use of \"codewords\" rather than keywords, such as enjo kosai, marijuana, and methamphetamine, to camouflage their criminal intentions. Research suggests that these codewords change once they become popular; therefore, searching for a specific word requires significant effort to keep track of the latest codewords. In this study, we focused on the appearance of codewords and those likely to be included in incriminating posts with aim to detect codewords with the high likelihood of inclusion in incriminating posts. We proposed new methods for detecting codewords based on differences in word usage and conducted experiments on concealed-word detection in order to evaluate method effectiveness. The results showed that the proposed method was capable of detecting concealed words other than those in the initial list and to better degree relative to baseline methods. These findings demonstrated the ability of the proposed method to rapidly and automatically detect codewords that change over time and blog posts that induce crimes, thereby potentially reducing the burden of continuous monitoring of codewords.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128401919","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231083
Cai Honghao, Yang Yingkun, Qu Yi
Entering the 5G era, the hardware design of low-density parity-check (LDPC) codes under the new standard has increasingly higher requirements on throughput. According to the geometric characteristics of the check matrices of the quasi-cyclicQC) LDCP codes under the CCSDS standard, this paper designs a layered dynamic normalized minimum sum algorithm(LDNMSA) under the pre-termination decoding based on de-layering scheme. The check matrix structure of this code is easy for hardware design and the storage resource consumption is low. The approximate replacement of the minimum sum algorithm(MSA) reduces the computational complexity of the check node update process. The determination of correction factors and inter-layer deletion thresholds through computer simulation improves the decoding performance and the speed of a single iteration, and adopt the layered scheduling scheme with optimized update order reduces the number of iterations required for decoding. Experimental results show that when the bit error rate(BER) is 10-5, the designed algorithm has a gain of approximately 0.5 dB compared to the MSA. The speed and calculation amounts to a single iteration are much lower than the log-likelihood-ratio(LLR) belief propagation(BP) algorithm and the performance is only less than 0.1dB.
{"title":"A Low Complexity Decoding Algorithm Design Based on Quasi-Cyclic LDPC Codes","authors":"Cai Honghao, Yang Yingkun, Qu Yi","doi":"10.1109/iCCECE49321.2020.9231083","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231083","url":null,"abstract":"Entering the 5G era, the hardware design of low-density parity-check (LDPC) codes under the new standard has increasingly higher requirements on throughput. According to the geometric characteristics of the check matrices of the quasi-cyclicQC) LDCP codes under the CCSDS standard, this paper designs a layered dynamic normalized minimum sum algorithm(LDNMSA) under the pre-termination decoding based on de-layering scheme. The check matrix structure of this code is easy for hardware design and the storage resource consumption is low. The approximate replacement of the minimum sum algorithm(MSA) reduces the computational complexity of the check node update process. The determination of correction factors and inter-layer deletion thresholds through computer simulation improves the decoding performance and the speed of a single iteration, and adopt the layered scheduling scheme with optimized update order reduces the number of iterations required for decoding. Experimental results show that when the bit error rate(BER) is 10-5, the designed algorithm has a gain of approximately 0.5 dB compared to the MSA. The speed and calculation amounts to a single iteration are much lower than the log-likelihood-ratio(LLR) belief propagation(BP) algorithm and the performance is only less than 0.1dB.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280713","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231211
Naina Chugh, N. Phumchusri
The overarching goal of this paper is to gain visibility on tourist preferences and whether or not the needs of tourists are being met. With the Travel and Tourism (T&T) sector being the backbone to the global economy and the sector becoming more saturated and competitive, insights on T&T are vital now, more than ever. The rise of social media and user-generated content has effectuated the opportunity for a systematic analysis of tourist preferences via user-generated content. This paper is focused on gaining insights of tourism in Bangkok, Thailand through user-generated content scraped from TripAdvisor's online reviews of tours and activities. In order to develop insights on tourist preferences and tourism trends in Bangkok, various analyses were implemented, including sentiment analysis to gather tourist point-of-view, association rules mining to find patterns of preferences, and natural language processing along with text frequency analysis to understand what features tourists are most frequently talking about. This paper also developed prediction models using logistic regression to forecast 5-start ratings and 1-star ratings of reviews - with the purpose of identifying factors that significantly affect position and negative sentiments on tours/activities.
{"title":"Bangkok Tours and Activities Data Analysis via User-Generated Content","authors":"Naina Chugh, N. Phumchusri","doi":"10.1109/iCCECE49321.2020.9231211","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231211","url":null,"abstract":"The overarching goal of this paper is to gain visibility on tourist preferences and whether or not the needs of tourists are being met. With the Travel and Tourism (T&T) sector being the backbone to the global economy and the sector becoming more saturated and competitive, insights on T&T are vital now, more than ever. The rise of social media and user-generated content has effectuated the opportunity for a systematic analysis of tourist preferences via user-generated content. This paper is focused on gaining insights of tourism in Bangkok, Thailand through user-generated content scraped from TripAdvisor's online reviews of tours and activities. In order to develop insights on tourist preferences and tourism trends in Bangkok, various analyses were implemented, including sentiment analysis to gather tourist point-of-view, association rules mining to find patterns of preferences, and natural language processing along with text frequency analysis to understand what features tourists are most frequently talking about. This paper also developed prediction models using logistic regression to forecast 5-start ratings and 1-star ratings of reviews - with the purpose of identifying factors that significantly affect position and negative sentiments on tours/activities.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121371590","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231224
Omar Hanif, Medha Chatterjee, Nihar Deshpande, Abhishek Bhatnagar, G. U. B. Babu
Nonlinear control study has evolved owing to the complexity of systems in multi-faceted disciplines. The most effective method of dealing with nonlinear systems is through linearization. Another revolution in the field of control is identifying the system with the help of fractional-order differential equations. Later, the fractional-order transfer function is calculated and controlled with the help of fractional-order controllers. This paper is a comprehensive work on a nonlinear system by taking a spherical tank case study. The work models the latter into multi-model integer order transfer functions (IOTF) then converts them into fractional-order transfer functions (FOTFs) via the frequency-domain method. It uses the Kalman filter algorithm to estimate the outputs of the various models of the multi-model bank. It then designs controllers, namely Proportional-Integral-Derivative (PID), Fractional-Order Proportional Integral Derivative (FOPID), and Multi-term Fractional-Order PID (MFOPIDs), using genetic algorithm. Subsequently, the paper thoroughly compares servo, regulatory, and robust responses of the PID controller and its variants.
{"title":"Design and Analysis of Fractional-Order PID Controller and its variants for Nonlinear Process using Kalman Filter","authors":"Omar Hanif, Medha Chatterjee, Nihar Deshpande, Abhishek Bhatnagar, G. U. B. Babu","doi":"10.1109/iCCECE49321.2020.9231224","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231224","url":null,"abstract":"Nonlinear control study has evolved owing to the complexity of systems in multi-faceted disciplines. The most effective method of dealing with nonlinear systems is through linearization. Another revolution in the field of control is identifying the system with the help of fractional-order differential equations. Later, the fractional-order transfer function is calculated and controlled with the help of fractional-order controllers. This paper is a comprehensive work on a nonlinear system by taking a spherical tank case study. The work models the latter into multi-model integer order transfer functions (IOTF) then converts them into fractional-order transfer functions (FOTFs) via the frequency-domain method. It uses the Kalman filter algorithm to estimate the outputs of the various models of the multi-model bank. It then designs controllers, namely Proportional-Integral-Derivative (PID), Fractional-Order Proportional Integral Derivative (FOPID), and Multi-term Fractional-Order PID (MFOPIDs), using genetic algorithm. Subsequently, the paper thoroughly compares servo, regulatory, and robust responses of the PID controller and its variants.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115601860","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}
Pub Date : 2020-08-17DOI: 10.1109/iCCECE49321.2020.9231135
Will Serrano
This paper presents Long Short-Term Memory (LSTM) in iBuilding: Artificial Intelligence in Intelligent Buildings. LSTM networks are widely used in time series data as their learning algorithm does not present exploding and vanishing gradient descent issues as traditional recurrent neural networks with back propagation learning algorithms. This paper proposes the use of LSTM networks to predict the values of the different iBuilding variables, such as environmental conditions, energy consumption or occupancy. Intelligent Buildings are used as an investment portfolio, Technology and Artificial Intelligence plays a critical role to make a successful Return on Investment (ROI). The business case and main driver to use Artificial Intelligence in Intelligent Buildings is to predict the future value of iBuilding variables therefore preventive action can be taken in the present to reduce OPEX costs such as decreasing overnight heating due low predicted low occupancy or preventive maintenance on mechanical and electrical assets such as lifts with fault detection and diagnosis. The predictions of the proposed LSTM in iBuilding has been validated with several public datasets against other predictors. The obtained results demonstrate that LSTM networks are more accurate than the Linear Regression (LR) model, typically used within the embedded predictors found on common spreadsheet software.
{"title":"Long Short-Term Memory in Intelligent Buildings","authors":"Will Serrano","doi":"10.1109/iCCECE49321.2020.9231135","DOIUrl":"https://doi.org/10.1109/iCCECE49321.2020.9231135","url":null,"abstract":"This paper presents Long Short-Term Memory (LSTM) in iBuilding: Artificial Intelligence in Intelligent Buildings. LSTM networks are widely used in time series data as their learning algorithm does not present exploding and vanishing gradient descent issues as traditional recurrent neural networks with back propagation learning algorithms. This paper proposes the use of LSTM networks to predict the values of the different iBuilding variables, such as environmental conditions, energy consumption or occupancy. Intelligent Buildings are used as an investment portfolio, Technology and Artificial Intelligence plays a critical role to make a successful Return on Investment (ROI). The business case and main driver to use Artificial Intelligence in Intelligent Buildings is to predict the future value of iBuilding variables therefore preventive action can be taken in the present to reduce OPEX costs such as decreasing overnight heating due low predicted low occupancy or preventive maintenance on mechanical and electrical assets such as lifts with fault detection and diagnosis. The predictions of the proposed LSTM in iBuilding has been validated with several public datasets against other predictors. The obtained results demonstrate that LSTM networks are more accurate than the Linear Regression (LR) model, typically used within the embedded predictors found on common spreadsheet software.","PeriodicalId":413847,"journal":{"name":"2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127279694","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}
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