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Smart Dome 4.0: Low-Cost, Independent, automated Energy System for Agricultural Purposes enabled by Machine learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd international Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： learning algorithms

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EFwork: An Efficient Framework for Constructing a Malware Knowledge Graph

EFwork: An Efficient Framework for Constructing a Malware Kn...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chen Chen Chunhe Xia Tianbo Wang Wanshuang Lin Yuan Zhao Yang Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Malware Knowledge Graph (MKG) serves as an essential auxiliary tool for malware detection and analysis. However, the construction of MKG faces several challenges, such as inadequate dataset quality, incomplete entity feature extraction, and the limitations imposed by deep learning techniques. To address these issues, we present an Efficient Framework for constructing a malware knowledge graph (EFwork). Firstly, we build a High-Quality dataset (HQdataset) and introduce a metric for data quality assessment based on knowledge coverage, timeliness, and density. Subsequently, we develop a Named Entity Recognition (NER) model that extracts character features, part-of-speech features, and word features from the data, leveraging deep learning models to identify malware-related entities. Finally, we implement a rule-based filtering mechanism, utilizing a comprehensive Rule database to eliminate entities that do not conform to predefined rules. Experimental result shows that our HQdataset demonstrates superior data quality when compared to other open-source datasets. Furthermore, our NER model combined with our Rule database outperforms existing models, achieving improvements of 0.67%, 0.74%, and 0.69% in Precision, Recall, and F1-Score, respectively.

关键词： Measurement Deep learning databases Filtering data integrity Knowledge graphs Feature extraction

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Traffic Sign Recognition Based on Multi-branch Residual Mixture Model

Traffic Sign Recognition Based on Multi-branch Residual Mixt...

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international conference on Communication Technology (ICCT)

作者： Hu Chaoqi Li Yaohui Fu Zehao School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China

In order to improve the accuracy of traffic sign recognition in real environment, a multi-branch residual mixture model based on LeNet-5 is proposed. Firstly, erosion and dilation in morphology are used to preprocess images. Secondly, two relatively independent convolutional branches are constructed to extract the multi-scale feature information of the images. Based on the idea of residual connection, bottleneck blocks are constructed to increase the depth of the network to improve the fitting ability of the model. In addition, Squeeze-and-Excitation block is added to the improved algorithm to reinforce the useful features, and Dropout is added to alleviate the overfitting. Finally, the hyperparameters are selected by using the five-fold cross-validation, and the exponential decay learning rate is used instead of the original fixed learning rate, so that the model can converge better. The accuracy of the improved algorithm on GTSRB and BelgiumTSC is better than that of the classical LeNet-5 model.

关键词： Training Image recognition Fitting data preprocessing Morphology Mixture models Interference

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Myocarditis Diagnosis: A Method using Mutual learning-Based ABC and Reinforcement learning

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

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international Symposium on Computational Intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics Computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

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IoT-Based Analysis of Environmental and Motion data for Comfort and Energy Conservation in Optimizing HVAC Systems 25th

IoT-Based Analysis of Environmental and Motion Data for Comf...

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Abdulwaheed, Badmus McGarry, Ken Eliot, Neil Baglee, David School of Computer Science Faculty of Technology University of Sunderland Sunderland United Kingdom School of Engineering Faculty of Technology University of Sunderland Sunderland United Kingdom

ISBN: (纸本)9783031859229

Growing energy consumption from campus infrastructure including lecture halls that run heating, ventilation and air conditioning (HVAC) systems motivates data-driven optimization. This research demonstrates an integrated application of Internet of Things (IoT) sensors and cloud-hosted predictive data analytics to enable smart lecture room policies improving efficiency and sustainability. A Raspberry Pi Pico W IoT device was interfaced with BME680 sensor for temperature, humidity and air quality data. The device also incorporated a PIR sensor for occupancy detection and Wi-Fi connectivity to transmit multivariate time series data. The prototype was installed in a university lecture room for real-time data capture. data was directed to a cloud analytics pipeline including MySQL storage and Node-RED for pre-processing. Time series forecasting was conducted by training autoregressive integrated moving average (ARIMA), Prophet and machine learning models on historical data to predict temperature, occupancy levels, and usage patterns 24 h into the future. An interactive dashboard visualized both real-time streams and model forecasts using Grafana for analytical insights. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive analytics

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Model-free PAC Time-Optimal Control Synthesis with Reinforcement learning

Model-free PAC Time-Optimal Control Synthesis with Reinforce...

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ACM and IEEE international conference on Formal Methods and Models for Co-Design (MEMOCODE)

作者： Mengyu Liu Pengyuan Lu Xin Chen Oleg Sokolsky Insup Lee Fanxin Kong Department of Computer Science and Engineering University of Notre Dame Notre Dame IN Department of Computer and Information Science University of Pennsylvania Philadelphia PA Department of Computer Science University of New Mexico Albuquerque NM

ISBN: (数字)9798350378023

ISBN: (纸本)9798350378030

Reaching a target safely and quickly is a control goal pursued by various applications, such as post-disaster rescue robots and industrial shipment. However, it is hard to formally guarantee safety and time-optimality under unknown dynamics via model-free controller synthesis algorithms. As a response, we propose a model-free reinforcement learning (RL) algorithm that synthesize a controller to reach a predefined target set of states with a probabilistic guarantee of time optimality, i.e., the actual reaching time is bounded close to the shortest time possible with high probability, and the bound becomes tighter when more training data is sampled. Our algorithm leverages a reward function that based on signal temporal logic (STL) robustness to reward fast reaching. With this reward function, we prove that Probably Approximately Correct (PAC) optimality in the state-value function implies PAC optimality in reach time. Then, we build our algorithm by extending Deplayed Gaussian Process Q learning (DGPQ) algorithm with a safety margin to protect the controlled agent. Consequently, our algorithm guarantees safety and a PAC bound in recovery time. Experiments show our method can achieve $\mathbf{9 7. 7 \%}$ success rate to reach the target with in the maximum time tolerance and outperform baselines.

关键词： Heuristic algorithms Training data Approximation algorithms Probabilistic logic Picture archiving and communication systems Robustness Stability analysis Safety System analysis and design Rescue robots

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Adopting Misclassification Detection and Outlier Modification to Fault Correction in Deep learning-Based Systems

Adopting Misclassification Detection and Outlier Modificatio...

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IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Chuan-Min Chu Chin-Yu Huang Neil C. Fang Department of Computer Science National Tsing Hua University Hsinchu Taiwan

Over the past few decades, researchers in software engineering (SE) have focused on testing, analyzing, repairing, and generating programs automatically and effectively. Today, combining neural networks and traditional software engineering techniques has major potential to benefit software quality and productivity. Regarding the development of neural networks, deep learning (DL) and convolution neural networks (CNNs) have been widely adopted by software applications for making decisions or providing suggestions. Considering life-critical DL-based applications, there is a need to correct the wrong decisions made by DL systems immediately. Therefore, we propose a novel fault-correction framework for alleviating potential misclassification issues of DL systems called the Outlier Modification for DL Systems (OMDLS). Our experiment results with two public datasets using different scales and label numbers to show that modifying the outliers based on the misclassification pairs can improve accuracy by up to 2.12% without retraining the model and modifying the inference immediately.

关键词： Deep learning Weight measurement Neural networks Training data Software quality Software reliability Security

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Analysing agricultural information through machine learning and artificial intelligence for SMART IRRIGATION

Analysing agricultural information through machine learning ...

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international conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： C. Lakshmi Abhitha Pagadala Sanjana Mythri Bandam Abitha Penugonda Bhavana Gangaraju Thanuja Ganapavarapu Rengarajan Amirtharajan V Thanikaiselvan Hemalatha Mahalingam School of EEE SASTRA Deemed university Thanjavur India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Traditional agriculture has been the global foundation for development for centuries. However, to meet this demand and the exponential growth of the population, farmers need water to irrigate their property. Farmers require a fix that modifies their business practices because of the scarcity of using this resource. To keep up with and satisfy Demand, Agriculture 4.0 has become a reality thanks to new technologies. automated Irrigation: Smart irrigation systems can automatically adjust schedules based on real-time data, such as weather conditions, soil moisture levels, and evapotranspiration rates. This helps farmers apply water more efficiently, reducing water waste and improving crop health. By collecting and analysing agricultural information through a combination of the Internet of Things and artificial intelligence, decisions have become increasingly precise to make decision-making easier. A cost-effective, intelligent, and adaptable irrigation strategy that can be used in a variety of settings is presented in this paper. For smart agriculture, machine learning algorithms are the foundation for this strategy. MongoDB and the Node-RED platform were used. We created an acquisition map from a collection of sensors (soil moisture, temperature, and rain) in a setting that guaranteed improved plant development for months. Based on our data, we used many different models: SVM, Simple Bayes, KNN, and Regression using logit. The outcomes showed that K-Nearest Neighbours outperformed other models (LR, SVM, NB) with a 98.6% identification rate and a 0.12 root mean square error (RMSE). In addition, we finally made available the online tool that combines the predictions made by our models with the different data released by the sensors for improved environmental visualisation and control.

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Reinforcement learning-Based Stream Reservation for Distributed Time Sensitive Networks

Reinforcement Learning-Based Stream Reservation for Distribu...

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international conference on Communication Technology (ICCT)

作者： Fengyou Xu Danfeng Qiu Bo Lv College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Beijing Institute of Astronautical Systems Engineering Beijing China

In order to achieve the pursuit of deterministic delay and unified network transmission in the industrial automation field, the time-sensitive networking group has developed a stream reservation mechanism to reserve the required bandwidth for time-sensitive traffic. In the distributed network configuration model, how to configure per-hop delay guarantee properly has become a key problem to restrict network performance, the reason is that an excessive loose or tight guarantee will reduce the streams that can be added to the network. Therefore, this paper proposes a stream reservation method based on reinforcement learning which can dynamically adjust the per-hop delay guarantee to improve the acceptance of streams. For this, it firstly defines the optimization problem of network configuration in the reservation process and give the per-hop delay model. Then the operation of the algorithm on bridge's data plane and control plane is simulated. We evaluate the performance of the algorithm with acceptance rate and bandwidth utilization in different scenarios, and the results demonstrate the effectiveness of the proposed algorithm.

关键词： Automation Heuristic algorithms Reinforcement learning Bandwidth Communications technology Delays Optimization

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Towards a Deep learning-Driven Service Discovery Framework for the Social Internet of Things: a Context-Aware Approach 22nd

Towards a Deep Learning-Driven Service Discovery Framework f...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Aljubairy, Abdulwahab Alhazmi, Ahoud Zhang, Wei Emma Sheng, Quan Z. Tran, Dai Hoang Macquarie Univ Sydney NSW 2109 Australia Macquarie Univ Intelligent Comp Lab ICL Lab Sydney NSW Australia Univ Adelaide Adelaide SA 5005 Australia

ISBN: (纸本)9783030915605;9783030915599

The Social Internet of Things (SIoT) is a new paradigm that enables IoT objects to establish their own social relationships without human intervention. A fundamental perspective of SIoT is to make socially capable objects, wherein objects can automatically share their services capability and exchange their experience with each other for the humans' benefit. Service discovery is a crucial task that requires fast, scalable, dynamic mechanisms. This paper aims to investigate the feasibility of adopting state-of-the-art deep learning techniques to build a social structure among IoT objects and design an effective service discovery process. To achieve this goal, we propose a framework that includes three phases: i) collecting information about IoT objects;ii) constructing a social structure among IoT objects using;and iii) developing an end-to-end service discovery model using the language representation model BERT. We conducted extensive experiments on real-world SIoT datasets to validate our approach, and the experimental results demonstrate the feasibility and effectiveness of our framework.

关键词： Social Internet of Things Service discovery Graph neural networks Natural language processing

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