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Deep Water Culture using Automated Hydroponic Systems

Deep Water Culture using Automated Hydroponic Systems

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international conference on Edge computing and Applications (ICECAA)

作者： M. Sivaleela M.Naveen Babu Umamaheswari Ramisetty G.V. Nagesh Kumar P.K. Dhal Vempalle Rafi Department of Electrical and Electronics Engineering Vel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology Chennai India Vignan's Institute of Information Technology Visakhapatnam JNTUA College of Engineering Pulivendula Kadapa India

Growing in sophistication, hydroponic farming is now considered to be the way of the future of agriculture. By detecting its governing factors, automated farms produce crops of a higher grade. This article introduces a hydroponic farming automation system. Utilizing a network of sensors, the goal is to atomize crop monitoring during the growing phase. A farm's water level and humidity may be monitored and controlled with the use of this network. By sending instructions via a messaging system, these actual occurrences are managed. The suggested system has minimal sensor value variations, which aids in automated control, according to the testing results. This activity mostly involves collecting ecological element data from remote sensors and sending it to the database utilizing remote protocols. Through the use of a message system, a system produces suitable conclusions regarding various nutrient levels and informs the farmer. The creation of a prototype enables the transmission of precise decisions to the end customers.

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DIME: Distributed Inference Model Estimation for Minimizing Profiled Latency

DIME: Distributed Inference Model Estimation for Minimizing ...

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international conference on Smart computing (SMARTCOMP)

作者： Robert Viramontes Azadeh Davoodi Department of Electrical and Computer Engineering University of Wisconsin - Madison Madison WI

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

Distributed inference allows minimizing metrics such as latency by offloading some computations from an edge device. It is commonly formulated and solved as an Integer Linear Program (ILP) for layer-wise partitioning of a Deep Neural Network (DNN) to decide transition points from an edge device to a hub and/or cloud devices. The formulation requires parameters reflecting latencies to execute different bundles of consecutive layers of DNN on each device. Profiling is the main way to measure these bundle latencies accurately on a device. In this work, we show a recent ILP of the layer-wise partitioning (JointDNN) cannot in fact always generate an optimal solution. As we show, this happens due to profiling behavior seen in some devices. We propose DIME (Distributed Inference Model Estimation) with novel modifications to accurately estimate the latency of a bundle within the ILP formulation. It guarantees generating the optimal solution regardless of the type of device in the network. Additionally, DIME incorporates a new input parameter within the ILP to control the tradeoff between solution quality and the profiling effort. In our experiments we show solving DIME always results in the optimal solution, sometimes with significantly less profiling effort.

关键词： Measurement Computational modeling Estimation Artificial neural networks

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A Grading Model for Jasmine Flowers Using Deep Learning and Quantum Machine Learning

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： K P Pratiksha Cauvery R Thrisha T Harshitha Suman Panigrahi Senthil Kumar Thangavel K Somasundaram C.P. Boopathy Department of Mathematics Amrita School of Physical Sciences Coimbatore Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Tamil Nadu India Department of Elelctical and Electronics Engineering Amrita School of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture.

关键词： Deep learning Visualization Sentiment analysis Accuracy Computational modeling Scalability Quality control Manuals Flowering plants Convolutional neural networks

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Efficient Structural Damage Detection Using Joint Vibration Signals and 1D-CNN Mode

Efficient Structural Damage Detection Using Joint Vibration ...

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international automatic control conference

作者： Chien-Chih Kuo Ching-Hung Lee Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

In existing detection methodologies, identifying structural damage often necessitates the deployment of multiple sensors. This study introduces a novel approach to structural damage detection, focusing on the joints of planar steel frameworks. The proposed method aims to address the issue of excessive sensor requirements. Initially, we capture vibration signals from the joints using sensors. Subsequently, by analyzing the signal discrepancies between damaged and undamaged joints, we achieve the substitution of vibration signals from damaged joints. This method effectively reveals the characteristic features of structural damage. Furthermore, we explore various combinations of sensors and employ a one-dimensional Convolutional Neural Network model (1D-CNN) for detecting structural damage. In practical implementation, we validate the viability of our proposed approach using benchmark data from the Qatar University Grandstand Simulator (QUGS). Results demonstrate that our method significantly reduces sensor requirements, utilizing only 40% of the total count. Additionally, employing a single 1D-CNN model enhances detection accuracy to an impressive 96.53%. This straightforward yet powerful approach not only enhances detection performance but also optimizes sensor utilization, ultimately elevating detection efficiency. In conclusion, our proposed method not only embodies simplicity but also yields remarkable advancements in structural damage detection. It further optimizes sensor deployment, thus amplifying overall detection performance.

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Improved Inception Network for wild mammal Behavior Recognition 6

Improved Inception Network for wild mammal Behavior Recognit...

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6th international conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Deng, Shichao Tang, Guizhong Mei, Lei College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9781450397148

The wildlife resources are significantly important parts of the ecosystem, and protecting wildlife resources is vital to the environment on which people live. Therefore, the behavior analysis of wild animals has become an important initiative to protect wild animals. This paper proposes a convolutional neural network architecture based on spatial-temporal information for action recognition of wild mammal. Since pixel-based object segmentation methods cannot eliminate the influence of background, we use the contour-based method Deep Snake to detect the animal contours in images as spatial features. The skeleton-based animal action recognition model is used to extract the joint coordinates during consecutive frames, then the fluctuate of the joint coordinates is used to distinguish the diversity of different behaviors of wild mammal in temporal space, which helps to characterize the difference of joint point movement speed of different behaviors. In addition, we also compute leg joint angle for distinguishing the behaviors running and standing. Finally, the temporal features and spatial features are fused into the convolutional neural network for action recognition of wild mammal. The experiments analyze the effect of the joint point angle, contour features, joint coordinates as well as their fusion features for wild mammal behavior recognition. It is concluded that the fusion features of coordinate fluctuate of joint points during consecutive frames, contour features and knee joint angle can significantly improve the accuracy of wild mammal action recognition. The model can effectively recognize four representational behaviors of animals: running, sitting, walking, and standing. The average accuracy of the proposed scheme for recognizing behavior of wild mammal achieve 95.5%. © 2022 Association for computing Machinery.

关键词： Mammals

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Design of Calligraphy Aesthetic Evaluation Model Based on Deep Learning and Writing Action 6th

Design of Calligraphy Aesthetic Evaluation Model Based on De...

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6th international conference on computing, control, and Industrial engineering, CCIE 2021

作者： Wang, Zhaoyi Lv, Ruimin School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9789811939266

Calligraphy is one of the best traditional arts in China and is widely spread. As the art of calligraphy has developed, the demand for calligraphy evaluation has also gradually increased. However, the traditional way of evaluating calligraphy works was time-consuming and subjective. In order to explore a more reasonable and effective way to evaluate calligraphy, a new data set containing images of calligraphy works and sequential data of writing actions was constructed using a nine-axis sensor to complete the writing action perception device, and the two types of data were applied to the calligraphy rating task. After the experimental analysis, the proposed model of Long-Short Term Memory network combined with K-Nearest Neighbor algorithm for the transport pen sequence data achieved 82.3% accuracy, which was significantly better than other baseline models. This shows that the writing action data has a better ability to evaluate calligraphy than the calligraphic images, which can be a guide for future computerized calligraphy evaluation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Pilot Study on Using Hybrid–Cascade Filtering on Brain Signals for the control Purposes

Pilot Study on Using Hybrid–Cascade Filtering on Brain Sign...

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international conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mariusz Pelc Dariusz Mikołajewski Edward Jacek Gorzelańczyk Anna Wieczorek Henryk Racheniuk Adam Sudoł Kayhan Latifzadeh Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

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Future Agriculture Farm Management Using Augmented Reality: A Study 4

Future Agriculture Farm Management Using Augmented Reality: ...

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4th international conference on Cognitive computing and Information Processing, CCIP 2022

作者： Sneha, T. Nethravathi, B. Shahapure, Nagamani H Nagashree, S. Shashidhara, Sinchana S Vilcart Solutions Pvt Ltd Bangalore India JSS Academy of Technical Education Dept. of ISE Bangalore India Syracuse University Dept. of Electrical Engineering and Computer Science NY13210 United States

ISBN: (纸本)9781665456487

Augmented reality is a live direct or indirect view of physical-word. 'Augment' by computer generated or extracted real word sensory. Augmented Reality supplement your word with digital object of any sort. It combines physical and virtual word. This is one of the immersive technologies that will change your lifestyle in the near future. AR technology has the positive opportunity and service to educate change. Agriculture has been an important source of food and has always been a very important aspect. Agriculture is highly labor-intensive and highly dependent on the knowledge of individual farmers, causing management problems. It can make a decisive input to the best possible operation management of AR. Improve farmers reality in insect research and pest control AR correction compared to orthodox techniques and teaching methods (circles, speeches, etc.). In the eyes of the overall public, agriculture has a tendency to be easier. This is just like the easy case of sowing and reaping seeds. Agriculture is absolutely the becoming a member of collectively of diverse sciences and is a completely complicated manufacturing system. The demanding situations dealing with in enterprise appear like exacerbated with the aid of using the extra of a well-skilled team of workers in at the upward thrust countries. It isn't unusual for uneducated humans to interact in agriculture the use of suspicious archaic techniques. Not surprisingly, they fail, which ends up in diverse demographic demanding situations for society. © 2022 IEEE.

关键词： Augmented reality

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A Novel Method for Studying Mosquito Oviposition Behaviour Using Computer Vision and Deep Learning Algorithm

A Novel Method for Studying Mosquito Oviposition Behaviour U...

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international conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Song-Quan Ong Pradeep Isawasan Gomesh Nair Khairulliza Ahmad Salleh Institute for Tropical Biology and Conservation Universiti Malaysia Sabah Sabah Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah Campus Malaysia School of Electrical and Electronics Engineering Universiti Sains Malaysia Penang Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Understanding the oviposition behaviour of mosquitoes is crucial for developing a vector surveillance program and a control strategy. To study this behaviour, in situ observation is one of the ways to determine the details of oviposition preference. However, this method of data collection is time-consuming and labour-intensive, and the presence of human observers often causes odour nuisance, which can lead to bias. We demonstrated a novel method that able to study this behaviour, which we named automatic Mosquito Oviposition Study System (AMOSS) that automatically detects and measures mosquito oviposition activity and collects data without human intervention. The system consists of a microcomputer with an infrared camera that records time-lapse video in a dark environment, and a post-record processing component for detecting the activity by using a deep learning algorithm. We used the system to study the oviposition activity of Aedes mosquitoes on a disposable mask and the result was consistent with the standard oviposition testing - egg counting bioassay. This technology could be an additional tool to determine mosquito preference for a particular substrate, which is very helpful in developing a push-and-pull strategy for mosquito control.

关键词： Deep learning Surveillance Object detection Observers Vectors Real-time systems Water resources Substrates Standards Testing

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Investigation of Potential Benefits and Challenges of U sing Gravity Energy Storage in Residential Sectors

Investigation of Potential Benefits and Challenges of U sing...

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2022 international Virtual conference on Power engineering computing and control: Developments in Electric Vehicles and Energy Sector for Sustainable Future, PECCON 2022

作者： Al-Hilfi, Laya M.A. Morris, Stella Fathima, A Peer Ezra, Morris Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering & Science Selangor Malaysia School of Electrical Engineering VIT University Chennai India

ISBN: (数字)9781665485920

ISBN: (纸本)9781665485920

Energy Storage System (ESS) has been integrated with renewable energy systems (RES) and fuel-based power plants to store energy during off-peak hours and serve users during peak hours. Its incorporation has resulted in game-changing advantages such as increased RES reliability, improved environmental conditions, supportive energy-generation mix, and so on. Nonetheless, Malaysia's energy storage market is dominated by battery energy storage systems (BESS), which has resulted in several problems, namely its massive carbon footprint and high cost, leading to the discovery of renewable energy storage systems (RESS). This study focuses on studying the benefits and challenges of gravity energy storage systems (GESS) in comparison to BESS. The GESS is a recently developed technology in the RESS industry. Even though it is still in the research and development stage, experts are optimistic about its potential benefits in storing unprecedentedly enormous amounts of electricity. The type of GESS studied in this research is the underground GESS with suspended weights (SGESS) which stores/generates energy by lifting/dispatching weights in a (500 - 5000) m shaft. The research findings supported GESS by demonstrating a surprising cost-effective conclusion when utilizing unused mines within Malaysia with no drilling required, rounding to a 2786 MYR (664 USD) Levelized Cost of Energy (LCOE), compared to BESS high LCOE of 7.2 MM MYR (1.72 MM USD) to store energy at the same community scale. Other benefits include lower operating and maintenance costs, less environmental damage, and a greater depth of discharge, to name a few, concluding this research by labeling GESS the most cost-effective, efficient, and environmentally friendly alternative to BESS. © 2022 IEEE.

关键词： Lithium-ion batteries

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