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检索条件"任意字段=5th International Conference on the Art, Science, and Engineering of Programming, Programming 2021"

共 1197 条记录，以下是1111-1120 订阅

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Design of Nearly Perfect Reconstruction Cosine Modulated Filter Banks Based on Eigenfilter

Design of Nearly Perfect Reconstruction Cosine Modulated Fil...

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international conference on Computer and Communications (ICCC)

作者： Xinyu Zhang Yicheng Wang School of Information Science and Engineering Lanzhou University Lanzhou China School of Mathematics and Statistics Lanzhou University Lanzhou China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

Traditional designs for M-channel cosine modulated filter banks usually involve optimization with many unknown parameters. As a result, it is difficult to design filter banks with high stopband attenuation. In contrast, a simple design approach that involves the optimization of only a single parameter is presented such that cosine modulated filter banks with high stopband attenuation can be easily obtained. In this method, the prototype filter of the M-channel cosine modulated filter banks is formulated as an eigenfilter. the cosine modulated filter banks can be efficiently designed by changing the passband edge frequency of the eigenfilter. Moreover, a simple time domain objective function based on the properties of Nyquist filter is used. Two design examples are presented to show that cosine modulated filter banks with high stopband attenuation can be obtained.

关键词： Filter banks Finite impulse response filters Prototypes Optimization Linear programming Filtering algorithms Attenuation

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Design of Quality Inspection System for Red Dates

Design of Quality Inspection System for Red Dates

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2017 5th international conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Ren Zhang Pengcheng Zhang Hongbing Meng College of Information Engineering Tarim University

In this paper, the near infrared spectroscopy analysis technology, combined with the traditional appearance of quality testing and chemical analysis methods, the establishment of red date quality characteristics of different quantitative digital calibration model. Based on the accumulated data of red date quality, the quality of red date such as sugar content, water content, color, texture and so on were digitized and quantitatively described, and the software of multi-parameter digital detection and evaluation of red date quality in Nanjiang was completed. And through the promotion and application in the enterprise gradually improve the quality of southern Xinjiang red date detection model. And lay the foundation for digitization and informatization of quality detection of red date in southern Xinjiang.

关键词： multi-objective bilevel programming genetic algorithm interpolation

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Multiple Disease Prediction Using ML

Multiple Disease Prediction Using ML

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Sameer Ray Sajid Ali Alok Katiyar School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Timely precise evaluation of any health concern is critical for the successful prevention and healing process of illnesses. therefore, the development of a medical diagnostic system based on machine learning (ML) algorithms can lead to more precise diagnoses than traditional methods. We have created a disease prediction system that uses ML to predict illnesses based on data or symptoms entered into the system, providing accurate results. this disease prediction system employs Python and machine learning programming language, with its Flask interface, and utilizes hospital database to predict diseases. By utilizing this predictive disease system using machine learning, we can improve diagnosis accuracy and support effective disease prevention and treatment.

关键词： Industries Machine learning algorithms Hospitals Databases Machine learning Forestry Prediction algorithms

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Calculation of the Optimum Parameters of Electrical Energy Storage and Generating Sources in Autonomous Local Electrical Systems

Calculation of the Optimum Parameters of Electrical Energy S...

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international conference on Modern Electrical and Energy Systems (MEES)

作者： Lina H. Hussienat Denys Myrhorod Syvenko Mykhailo Roman Buinyi Viacheslav Bezruchko Serhii Halko Department of Technical Science Al Balqa Applied University Al Salt Jordan Department of Electricity and Energy Management State Biotechnological University Kharkiv Ukraine Department of Electricity Supply and Energy Management State Biotechnological University Kharkiv Ukraine Department of Electrical Power Engineering Chernihiv Polytechnic National University Chernihiv Ukraine Department of Electrical Engineering and Electromechanics Dmytro Motornyi Tavria State Agrotechnological University Melitopol Ukraine

the paper presents the issue of determining the most the optimal parameters for electrical energy storage and generating equipment in autonomous local electrical systems (ALES). the substantiation of the role of energy storage devices in the presence of renewable energy sources in autonomous local electrical systems with a stochastic mode of operation is given. the main provisions of the used model of electrical systems are given. the dependence of the parameters of the energy storage device depending on the composition of generating capacities has been studied. the implementation of the problem of determining the parameters of electric energy storage devices by the method of purposeful simulation is considered. the results of the multi-criteria choice of power and energy intensity of the electric energy storage are substantiated. A mathematical model has been developed for determining the optimal parameters of electric energy storage devices for autonomous local electrical systems using mixed integer programming with optimization and taking into account economic indicators. the main provisions of the model used are considered. the dependences of the parameters of electric energy storage devices on the composition of the generation of autonomous local electrical systems have been studied.

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Mechatronics Design and Flight Simulation of Hexacopter Aerial Robot Coupled with Visual Detection System for Crop Diseases

Mechatronics Design and Flight Simulation of Hexacopter Aeri...

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Recent Trends in Computer science and Technology (ICRTCST), international conference on

作者： Kevin A. Paucar Enzo M. Gallo Ruiz Ricardo Palomares José Cornejo Professional School of Mechatronics Engineering Universidad Ricardo Palma Lima Peru Research Group of Robotics and Mechatronics (GI-ROMA) Universidad Ricardo Palma Lima Peru Universidad Tecnológica del Perú Lima Peru

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

the National Institute of Statistics and Informatics - INEI in the year 2021 indicated that the agricultural sector experienced a growth of 6.21%, with alfalfa production being one of the main drivers with 10.1%, positioning Peru as one of the countries with the highest growth in the region. In 2022, an exhaustive investigation was carried out in the Department of Moquegua, evidencing a deficiency in the agricultural sector in terms of the availability of technological tools and resources for the execution of innovative projects. In response to this, a hexacopter drone was designed and implemented using SolidWorks software and equipped with an artificial vision system. A detailed mechanical analysis was carried out to evaluate the materials used, carbon fiber, reaching a total mass of 08 kg for efficient flight. An electrical analysis was carried out to determine the power required by the drone, where, Tarot 4114/320KV motors, Hobbywing FlyFun variators, and a 10000 mAh LiPo battery that allows a flight autonomy of 25 minutes were used. It uses an Ardupilot APM 2.8 flight controller for programming the drone's flight paths using the Mission Planner software, likewise, Google Colab and Python were used for training neural networks and NVIDIA Jetson Nano for integrated processing applications of images, as well as, the KyAo application was implemented for the registration of the coordinates of the crops. By the end of 2023, the increased production of alfalfa will be validated through the early detection of pests and diseases in the Rinconada sector of the Moquegua Province in Peru with the participation of the farming community.

关键词： Training Visualization Crops Process control Production programming Software

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Simulation Testing Scenario Generation for Comfort Evaluation of Automated Vehicles

Simulation Testing Scenario Generation for Comfort Evaluatio...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Yuhang Yang Yafei Wang Chengliang Yin Qinghui Ji School of Mechanical Engineering Shanghai Jiao Tong University Shanghai CN

ISBN: (纸本)9781665408486

Simulation test is an essential step for the development in control strategy of automated vehicles. On the other hand, driving comfort of automated vehicles are getting more and more concerns. therefore, generating simulation test scenarios can accelerate the development and iteration of control strategies on both safety and comfort. However, a lot of current studies about test scenario generation focus on the safety of automated vehicles and few studies concern on the driving comfort evaluation. In this paper, a testing scenario generation framework for comfort evaluation of automated vehicles is proposed. A raw scenario library is generated by applying the multi-parameter gibbs sampling method according to the fitting distributions of the influence values in the specific scenario. To identify the comfort level of the generated raw scenario library, simulation test and comfort evaluation is taken. A vehicle dynamic model and a basic automated driving controller with AEB and ACC functions are adopted in the simulation test, and a random-forest model is established to give comfort scores based on the dynamic parameters during simulation test. To extracted critical challenging scenarios from the raw scenario library, an objective searching function is designed with a comfort level factor and a occurring frequency factor. A cut-in case is analyzed to demonstrate the proposed framework and a naturalistic dataset with 30,875 data fragments is adopted as reference. the results show that critical scenarios generated by the proposed framework are more challenging on driving comfort than the scenarios in naturalistic dataset.

关键词： Databases Life estimation Probability density function Search problems Sampling methods Linear programming Libraries

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Reinforcement Learning in Cognitive Robots for Autonomous Path Planning

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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Data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： Archana Das R. Indu Poornima G. Gokila Deepa K. Selva Sheela Akshya J Mani Deepak Choudhry Department of Information Science and Engineering New Horizon College of Engineering Bengaluru Department of Information Technology KGiSL Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science PPG Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. this research studies the integration of Reinforcement Learning in cognitive robots with Q-Learning for autonomous path planning. the study addresses the most critical issues in traditional A* and Dynamic programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-Learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. the Q-Learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. the model was found to be significantly better than its counterparts. In this context, as indicated, the Q-Learning model runs at 11.5 seconds, faster than A* at 18.3 seconds and Dynamic programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. the robustness and scalability of the Q-Learning model make it highly applicable in real-world applications.

关键词： Adaptation models Q-learning Accuracy Navigation Heuristic algorithms Real-time systems Dynamic programming Cognitive robotics Collision avoidance Robots

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Comparative Analysis of artificial Intelligence and its Powered Technologies Applications in the Finance Sector

Comparative Analysis of Artificial Intelligence and its Powe...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Swati Tyagi Tanvi Jindal Somanchi Hari Krishna Samar Mansoor Hassen Surendra Kumar Shukla Chamandeep Kaur University of Delaware Newark DE USA Chitkara Business School Chitkara University Punjab Department of Business Management Vignana Bharathi Institute of Technology Aushapur Village Ghatkesar Mandal Malkangiri Medchal Hyderabad Telangana India Computer Science Department Jazan University Saudi Arabia Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CS & IT Lecturer Jazan University Saudi Arabia

Banks and other financial institutions will be able to adopt innovative products and services, and more importantly, endure disruptions to their customers’ experience, thanks to advancements in artificial intelligence (AI). Financial institutions would struggle to thrive in today’s mechanized economy without the aid of fintech companies, which use innovative innovation to enhance or try and supplant human specialists with complex calculations. To keep a critical upper hand, banking and monetary organizations should take on man-made intelligence and integrate it into their business system and tasks. this examination venture will look at the elements of computer based intelligence environments in the banking and money industry and how they are quickly arising as a key disruptor by focusing on probably the most basic perplexing business worries around here. Several viewpoints exist on the potential of AI in this field, with the majority centering on the effects and relevance it will have on employment in the banking and financial services industry.

关键词： Technological innovation Employment Focusing Banking programming Security artificial intelligence

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Hardware-Friendly Activation Function Designs and Its Efficient VLSI Implementations for Transformer-Based Applications

Hardware-Friendly Activation Function Designs and Its Effici...

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IEEE international conference on artificial Intelligence Circuits and Systems (AICAS)

作者： Yu-Hsiang Huang Pei-Hsuan Kuo Juinn-Dar Huang Department of Electronics and Electrical Engineering Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan

the activation function is one of key elements in modern machine learning algorithms. However, some broadly-used activation functions are exceptionally complex, e.g., GELU in Transformer-based algorithms, which makes their precise yet efficient VLSI implementations extremely hard. In this paper, two series of hardware-friendly activation function designs, DNR and PWL, and their VLSI implementations are proposed. Both are specifically designed to replace GELU, which is widely used in Transformer-related applications. Instead of utilizing traditional lookup-table (LUT)-based approximation methods, this paper introduces new activation functions that are not only hardware-friendly but successfully alleviate the dying neuron issue. Besides, each series includes a number of members, which can be freely selected through programming to best fit a given application. Experimental results indicate that the proposed new activation functions achieve comparable or even better model accuracy as compared to GELU. Moreover, the highly efficient and flexible VLSI implementations support 16 different Q-formats to maximize the output precision under various input scales. Compared with approximation-based implementation strategies, the proposed activation function designs and the corresponding LUT-free hardware implementations do achieve a significant improvement in speed, area, and power.

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Using a Compositional Function Hybridization of GCDPSO and GA to Solve Task Allocation Problems in Smart Farms

Using a Compositional Function Hybridization of GCDPSO and G...

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Computing and Big Data (ICCBD), international conference on

作者： Minzhi Xu Weidong Li Xuejie Zhang School of Information Science and Engineering Yunnan University Kunming China School of Mathematics and Statistics Yunnan University Kunming China

Compared with cloud computing, edge computing has the advantages of low latency, reduced network pressure and network risk. As a result of its development, smart farms have also started to shift from cloud services-oriented to edge computing-oriented, which is to put the tasks of smart farms on edge servers. For some reasons, such as energy limitation, the number of tasks that the edge server can handle is limited in a period of time, so some tasks still need to be processed by the cloud server. When these tasks need to be processed not only by edge servers but also by cloud servers, they need to be divided into two parts, one part which requires as much cloud processing cost as possible is processed on the edge server, and the other part which requires as little cloud processing cost as possible is processed on the cloud server. In our model, task is divisible. To solve this task assignment problem, this study considered a functional composition integration approach, Hybridization of GCDPSO and GA (HGAGCDPSO), where GCDPSO is Guaran-teed Convergence Discrete Particle Swarm Optimization and GA is Genetic Algorithm. In this paper we compares HGAGCDPSO performance with GCDPSO, Greedy Algorithm and Dynamic programming. Results revealed that the overall performance of HGAGCDPSO is better than the other three algorithms.

关键词： Smart agriculture Cloud computing Costs Servers Resource management Task analysis Particle swarm optimization

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