2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)最新文献_第2页

Associative Memories Based on Spherical Seperability 基于球形可分离性的联想记忆

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574065

G. Ramamurthy, T. J. Swamy

引用次数: 2

Multi-Objective Differential Evolution with unbalanced Divide-and-Conquer Strategy for Warehouse Resource Allocation 基于非平衡分而治之策略的仓库资源配置多目标差分演化

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574070

Jahnavi Malagavalli, Sai Rohan Gowtham K, Abhimanyu Bellam, A. K. Bhattacharya, Karunakar Gadireddy, Ahmad Reza Shehabinia, Takatsugu Kobayashi

{"title":"Multi-Objective Differential Evolution with unbalanced Divide-and-Conquer Strategy for Warehouse Resource Allocation","authors":"Jahnavi Malagavalli, Sai Rohan Gowtham K, Abhimanyu Bellam, A. K. Bhattacharya, Karunakar Gadireddy, Ahmad Reza Shehabinia, Takatsugu Kobayashi","doi":"10.1109/ICETCI51973.2021.9574070","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574070","url":null,"abstract":"The Flexible Job Shop Scheduling Problem is well known as NP-hard and constrained. It is generic in the sense that many other problems are defined along similar lines. The Warehouse Resource Allocation problem belongs to this family, with its typically massive scale as an add-on factor. The severe non-concurrency constraint on both Tasks (Jobs) and Agents (Machines) remains valid. Here the problem is formulated in terms of the dual objectives of minimizing delay in completion of Tasks, and maximizing uniform utilization of available Agents. Differential Evolution (DE) is used for this multi-objective optimization, using both non-dominated sorting as well as variant linear combinations of multiple objectives. Efficacies of the two approaches are evaluated. The practical problem deals with thousands of Tasks represented as variables of the solution, which need to be loaded onto scores of Agents that appear as integer solutions of these variables. Since DE at basics is formulated to work on continuous spaces, here the integer solutions are mapped onto real spaces where DE is executed and then transformed back into integer space. Local Search is used to augment the baseline Global Search. A Divide-and-Conquer approach is implemented for the solution, with some novelty for handling variant, nonuniform sizes of different classes of Tasks and Agents.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134185843","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

Deep Learning for Self-tuning of Control systems 控制系统自整定的深度学习

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574048

Junaid Farooq, M. A. Bazaz

引用次数: 2

HAR Using Bi-directional LSTM with RNN 基于RNN的双向LSTM算法

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574073

N. Singh, Koduru Sriranga Suprabhath

{"title":"HAR Using Bi-directional LSTM with RNN","authors":"N. Singh, Koduru Sriranga Suprabhath","doi":"10.1109/ICETCI51973.2021.9574073","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574073","url":null,"abstract":"It is hard to monitor human activities in various contexts like security surveillance, healthcare, and human-computer interaction. Human Activity Recognition is the process of predicting what a person is doing based on the traces of their movement. We propose using deep recurrent neural networks (DRNNs) for building recognition models capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM), DRNNs and evaluate their effectiveness on various benchmark datasets. Long shortterm memory (LSTM) is an artificial deep recurrent neural network (DRNN) architecture used in deep learning, especially for time series prediction. It can process single data points (such as images) and entire data sequences (such as speech or video). LSTM networks are well-suited for classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between essential events in a time series in real-time. We proposed Human Activity Recognition (HAR) using a smartphone dataset and LSTM. Compared to a classical approach, using Deep Recurrent Neural Networks (DRNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network, which acts as a black box, modeling the problem correctly. This means that the neural networks are almost always able to identify the movement type correctly. We used jupyter notebook with python 3.7+.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128332445","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: 4

Cloud Architecture For IOT Based Bridge Monitoring Applications 基于物联网的桥梁监控应用的云架构

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574044

Visvesh Naraharisetty, Venkat Surendar Talari, Sairam Neridu, Prafulla Kalapatapu, Venkata Dilip Kumar Pasupuleti

{"title":"Cloud Architecture For IOT Based Bridge Monitoring Applications","authors":"Visvesh Naraharisetty, Venkat Surendar Talari, Sairam Neridu, Prafulla Kalapatapu, Venkata Dilip Kumar Pasupuleti","doi":"10.1109/ICETCI51973.2021.9574044","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574044","url":null,"abstract":"Structural Health Monitoring (SHM) is becoming an important research topic to improve human safety and to reduce maintenance costs. However, most of the existing SHM systems face challenges performing at real-time due to environmental effects and different operational hazards. Therefore, the Internet of Things (IoT) can be used, which would provide flexibility to monitor structures (building, bridge) from anywhere. AWS is a cloud computing platform used to connect IoT devices and store or analyze their data. In this paper an architecture is described to enable monitoring applications on a cloud computing platform AWS. However, IoT is the network of physical object devices, vehicles, buildings and other items embedded with electronics, sensors, and network connectivity-that enables objects to collect and exchange data. IoT allows objects to be sensed and controlled remotely across existing network infrastructure. Sensors (accelerometers) use Xbee protocol to send data to nodes (raspberry pi), then to the cloud using messaging protocols. The objective of the work demonstrates the architecture to carry out data collection, analysis, visualization of sensor data using AWS services that results in development of live monitoring standalone web interface.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470353","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: 4

2021 International Conference on Emerging Techniques in Computational Intelligence [Front matter] 2021年计算智能新兴技术国际会议〔简报〕

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/icetci51973.2021.9574062

引用次数: 0

Teaching and Learning the Principles of Wireless Communication through Smartphone and CRFO 通过智能手机和CRFO教与学无线通信原理

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574082

P. Chandhar, S. Babu, T. Elakkiya

引用次数: 0

Novel Approach for Memory Storage Systems with Chaos-Chaos Intermittency 混沌-混沌间歇存储系统的新方法

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574056

S. Nobukawa, Nobuhiko Wagatsuma, H. Nishimura, Keiichiro Inagaki, Teruya Yamanishi

{"title":"Novel Approach for Memory Storage Systems with Chaos-Chaos Intermittency","authors":"S. Nobukawa, Nobuhiko Wagatsuma, H. Nishimura, Keiichiro Inagaki, Teruya Yamanishi","doi":"10.1109/ICETCI51973.2021.9574056","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574056","url":null,"abstract":"In nonlinear systems with a barrier/threshold, the synchronization under a weak external input signal is strengthened using appropriate additive stochastic noise. This phenomenon is known as stochastic resonance. Recent progress in the application of stochastic resonance has shown that the presence of additive noise enhances memory functions in memory elements with bi-stable oscillations, even under extremely low power consumption. In addition to additive noise, deterministic chaotic behavior induces chaotic resonance, a phenomenon that is similar to stochastic resonance. Chaotic resonance emerges in nonlinear dynamical systems with chaos-chaos intermittency, where the chaotic orbit moves among separated attractor regions through an attractor-merging bifurcation. In previous studies, a higher sensitivity of chaotic resonance compared to that of stochastic resonance was reported. In this context, we hypothesized that memory devices based on chaotic resonance can be used to realize a novel device for storing memory with lower power consumption than in devices based on stochastic resonance. In this study, to prove this hypothesis, we induce the attractor-merging bifurcation in a cubic map system, which is the simplest model for emerging chaotic resonance. We use one approach for adjusting the internal system parameters under noise-free conditions and another for applying stochastic noise, which is similar to the conventional approach using stochastic resonance. By comparing the performance of these approaches, we reveal that the former exhibits a higher memory storing ability than the latter stochastic approach, even under weaker memory storage input signals. This superiority allows the development of memory devices with low power consumption. The method involving chaotic resonance facilitates the improvement of memory devices that were previously limited to the application of stochastic resonance.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133022955","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

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574058

C. S. Jyothi, B. Sandhya

{"title":"Estimating Similarity between Visual and Long Wave Infrared patches using Siamese CNN","authors":"C. S. Jyothi, B. Sandhya","doi":"10.1109/ICETCI51973.2021.9574058","DOIUrl":"https://doi.org/10.1109/ICETCI51973.2021.9574058","url":null,"abstract":"Image matching is the process of identifying correspondences between same scene images that differ due to different acquisition parameters such as illumination, viewpoint, or noise. Image patch matching involves computing similarity between the patches based on content invariant to various photometric or geometric variations. Our objective is to design a convolution neural network that computes similarity between visual and infrared image patches of same scene. Similarities of images are measured from the feature maps that are extracted from raw patches. A model is developed that maps the patch to low-dimensional feature vector and similarity is calculated using a fully connected layer which outputs the distance between patches. Threshold is applied on the similarity resulting ‘1’ for similar patches and ‘0’ for dis-similar patches. Siamese CNN architecture based on transfer learning with regression is built with convolution trained and tested for patch similarity. Network model is trained with illumination varying patches of Hpatches dataset and are evaluated with a dataset of corresponding visual and long wave infrared images.","PeriodicalId":281877,"journal":{"name":"2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127633121","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

Trajectory Tracking of a 2-DOF Helicopter System using Fuzzy Controller Approach 基于模糊控制器的二自由度直升机系统轨迹跟踪

2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI) Pub Date : 2021-08-25 DOI: 10.1109/ICETCI51973.2021.9574049

Abhishek Chaudhary, B. Bhushan

引用次数: 2