2022 IEEE World AI IoT Congress (AIIoT)最新文献

Computer Vision Method in Means of Egress Obstruction Detection 出口障碍物检测中的计算机视觉方法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817354

Ismail A Idowu, K. Nyarko, Otily Toutsop

{"title":"Computer Vision Method in Means of Egress Obstruction Detection","authors":"Ismail A Idowu, K. Nyarko, Otily Toutsop","doi":"10.1109/aiiot54504.2022.9817354","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817354","url":null,"abstract":"A safety inspection is an on-site walk-through to identify potential hazards to occupants and personnel and options for remedial action. Although the Common approach for the safety inspection (Means of Egress MOE) is manual, this approach is ineffective and inexhaustive due to some inherent challenges: (1) infrequent inspection, and (2) inefficient use of trained human resources. To address these challenges, we introduced a Dual Temporal Buffer Differencing method. This computer vision-based approach automates the inspection of an interior building hallway (exit access) for an obstruction that may be a potential fire hazard. Our approach is important because it will mitigate the risk of a fire hazard to the building occupants by sensing and alerting the safety officer before a situation turns into an emergency. The performance of our proposed approach, the benefits, and the implementation challenges, were evaluated through a case study. The result demonstrates that our proposed Dual Temporal Buffer Differencing (DTBD) method can detect a potential fire hazard in the building exit access effectively and continuously. As a result, the approach can facilitate safety in the building and allow safety inspectors to plan more trained human resources.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124362931","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

A Hybrid Firefly-DE algorithm for Ridesharing Systems with Cost Savings Allocation Schemes 具有成本节约分配方案的拼车系统的混合萤火虫- de算法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817152

Fu-Shiung Hsieh

{"title":"A Hybrid Firefly-DE algorithm for Ridesharing Systems with Cost Savings Allocation Schemes","authors":"Fu-Shiung Hsieh","doi":"10.1109/aiiot54504.2022.9817152","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817152","url":null,"abstract":"Ridesharing or shared mobility have been attracting significant attention in relevant research community. Most studies focus on how to match drivers and riders to minimize the overall travel distance based on their requirements. As cost savings is an essential function in ridesharing systems, allocation of cost savings has attracted researchers' attention recently. Several simple schemes have been proposed in the literature. For example, a simple scheme is to divide cost savings equally between driver and passengers in a ride. Another scheme is to allocate cost savings to participants proportional to their original travel distance. Although these simple schemes are easy to implement, there still lack a study that compare their effectiveness in ridesharing systems by applying different metaheuristic algorithms. In this paper, a hybrid meta-heuristic algorithm called hybrid Firefly-DE algorithm based on Differential Evolution and Firefly Algorithm will be adopted to match drivers and riders. We will compare three cost savings allocation schemes based on the numerical results. In our experiments, meta-heuristic algorithms are applied to find the matches to minimize the overall travel distance. The above schemes are then used to allocate cost savings among participants. The results indicate that the proportional cos savings allocation scheme is more effective than the other schemes to allocate cost savings equally between the drivers and the passengers.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153979","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: 3

An Automatic Speech Segmentation Algorithm of Portuguese based on Spectrogram Windowing 基于谱图窗的葡萄牙语自动语音分割算法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817299

Lap-Man Hoi, Yuqi Sun, S. Im

引用次数: 0

Classification of Movie Success: A Comparison of Two Movie Datasets 电影成功的分类:两个电影数据集的比较

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817158

Shreehar Joshi, Eman Abdelfattah, Ryan Osgood

引用次数: 1

Comparative Analysis of AlexNet, Resnet-50, and Inception-V3 Models on Masked Face Recognition AlexNet、Resnet-50和Inception-V3模型在蒙面人脸识别中的比较分析

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817327

Benedicta Nana Esi Nyarko, Wu Bin, Jinzhi Zhou, George Kofi Agordzo, J. Odoom, Ebenezer Koukoyi

{"title":"Comparative Analysis of AlexNet, Resnet-50, and Inception-V3 Models on Masked Face Recognition","authors":"Benedicta Nana Esi Nyarko, Wu Bin, Jinzhi Zhou, George Kofi Agordzo, J. Odoom, Ebenezer Koukoyi","doi":"10.1109/aiiot54504.2022.9817327","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817327","url":null,"abstract":"Since the outbreak of the coronavirus pandemic in December 2019, there has been increased interest in developing better facial recognition systems. This stems from the need to protect everyone from the spread of the virus. However, the measures taken to prevent the spread of the virus pose a challenge to security and surveillance systems as existing systems are unable to match faces with masks more efficiently. For this study, a custom dataset was generated due to the unavailability of a large face dataset for masked face recognition, and the existing datasets focused on Caucasians (white race faces) while Aethiopians (black race faces) were neglected. In this study, a comparative analysis was conducted between the AlexNet, ResNet-50, and Inception-V3 models to recognize faces with surgical masks, fabric masks, and N95 masks. The results of the study showed that the CNN models achieve excellent recognition accuracy for masked and unmasked faces. Analysis of the models' performance showed that the AlexNet model achieved 95.7%, ResNet-50 achieved 97.5%, and Inception-V3 also achieved 95.5%. From the study, ResNet-50 performed better than Inception-V3 and AlexNet models in recognizing masked faces.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128268127","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

Honeynets and Cloud Security 蜜网和云安全

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817263

Eric M Toth, M. Chowdhury

引用次数: 0

EEG and fNIRS Analysis Using Machine Learning to Determine Stress Levels 利用机器学习确定压力水平的EEG和fNIRS分析

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817318

J. D. L. Cruz, Douglas Shimizu, K. George

引用次数: 1

Survey on Types of Cyber Attacks on Operating System Vulnerabilities since 2018 onwards 2018年以来针对操作系统漏洞的网络攻击类型调查

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817246

Maame Araba Vander-Pallen, P. Addai, Stuart Isteefanos, Tauheed Khan Mohd

引用次数: 3

Detection of Faults in Electro-Hydrostatic Actuators Using Feature Extraction Methods and an Artificial Neural Network 基于特征提取和人工神经网络的电静液执行器故障检测

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817236

M. Ghanbari, W. Kinsner, N. Sepehri

{"title":"Detection of Faults in Electro-Hydrostatic Actuators Using Feature Extraction Methods and an Artificial Neural Network","authors":"M. Ghanbari, W. Kinsner, N. Sepehri","doi":"10.1109/aiiot54504.2022.9817236","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817236","url":null,"abstract":"Electro-hydrostatic actuators (EHAs) are a type of hydraulic actuators which use pumps rather than valves to control the motion. As a result, they are more efficient than the valve-operated actuators. This paper presents an AI-based internal leakage detection algorithm for a single-rod EHA. Actuator internal leakage has been chosen to demonstrate the efficacy of the algorithm. Based on the sensitivity of various measures to varying levels of internal leakage, indicators are derived from the easy to obtain pressure measurements and a fault decision algorithm for quantifying the level of internal leakage in the actuator is established. This paper presents a new architecture of an artificial neural network (ANN) for detecting the existence of an internal leakage fault as labelled data. First, a sensitivity analysis is used to select a measure candidate for further research. Second, the measure chosen is analyzed using feature extraction methods. This step aims to extract hidden features to maximize the internal leakage fault detection. Finally, the fault detection algorithm classification efficiency is assessed by studying the detection rate of the proposed architecture. The experimental results show that the developed algorithm can detect internal leakage faults with 99.46% accuracy.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115469008","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

Pattern Recognition Method for Detecting Engineering Errors on Technical Drawings 技术图纸工程错误检测的模式识别方法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817294

R. Dzhusupova, Richa Banotra, Jan Bosch, H. H. Olsson

{"title":"Pattern Recognition Method for Detecting Engineering Errors on Technical Drawings","authors":"R. Dzhusupova, Richa Banotra, Jan Bosch, H. H. Olsson","doi":"10.1109/aiiot54504.2022.9817294","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817294","url":null,"abstract":"Many organizations are looking for how to automate repetitive tasks to reduce manual work and free up resources for innovation. Machine Learning, especially Deep Learning, increases the chance of achieving this goal while working with technical documentation. Highly costly engineering hours can be saved, for example, by empowering the manual check with AI, which helps to reduce the total time for technical documents review. This paper proposes a way to substantially reduce the hours spent by process engineers reviewing P&IDs (Piping & Instrumentation Diagrams). The developed solution is based on a deep learning model for analyzing complex real-life engineering diagrams to find design errors - patterns that are combinations of high-level objects. Through the research on an extensive collection of P&ID files provided by McDermott, we prove that our model recognizes patterns representing engineering mistakes with high accuracy. We also describe our experience dealing with class-imbalance problems, labelling, and model architecture selection. The developed model is domain agnostic and can be re-trained on various schematic diagrams within engineering fields and, as well, could be used as an idea for other researchers to see whether similar solutions could be built for different industries.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115718718","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