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检索条件"主题词=Software Algorithms"

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Autonomous Detection and Approach Tracking of Moving Ship on the Sea by VTOL UAV based on Deep Learning Technique through Simulated Real-time On-Air Image Acquisitions

Autonomous Detection and Approach Tracking of Moving Ship on...

引用

National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)

作者： Tuan Do Trong Quan Tran Hai Manh Vu Van Binh Nguyen Thai Tung Nguyen Chi Truong Nguyen Quang School of Electrical and Electronics Engineering Hanoi University of Science and Technology Hanoi Vietnam Aerospace Electronics Laboratory School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (纸本)9781665410021

Real-time detection and tracking moving ships as well as locating the helipad on that ship is still a challenge in critical missions at sea. By using VTOL (Vertical Take-Off and Landing) UAV (Unmanned Aerial Vehicle) types, it allows both the ability to fly approaching the ship and also be able to land vertically on the helipad. This paper proposes a SITL (software In The Loop) system to verify the automatic detection and tracking of ships moving at sea for the VTOL UAV during the mission and an algorithm to guide VTOL UAV mode selection in the process of approaching and landing on ships moving at sea. On-air images collected from the VTOL UAV’s camera in the X-Plane 11 simulation environment are used to train Deep Learning computer vision algorithms. Real-time ship detection algorithm with up to 125 FPS and 96% accuracy. From the results of the ship and helipad detection, we propose an algorithm to assist the transition of flight modes of VTOL UAV during the tracking and landing mission on a moving ship at sea.

关键词： Deep learning Tracking loops Computer vision Computational modeling software algorithms Switches Autonomous aerial vehicles

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Lightweight Cryptography for Resource Constraint Devices: Challenges and Recommendation

Lightweight Cryptography for Resource Constraint Devices: Ch...

引用

Cyber Resilience Conference (CRC)

作者： Ahmad Mustafa Mohamad Al-Aboosi Samar Kamil Siti Norul Huda Sheikh Abdullah Khairul Akram Zainol Ariffin Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (数字)9781665418447

ISBN: (纸本)9781665448239

In the recent few years, data communication through the Internet of Things (IoT) network is increased exponentially. However, the data is prone to several attacks on the network. The most popular attacks are eavesdropping, replay, man-in-the-middle attack, etc. To prevent these attacks, cryptography algorithms are used. The devices are deployed in the IoT network are resource constraint, limited memory, and low battery life. Therefore, the National Institute of Standard and Technology (NIST) recommended the lightweight cryptography algorithm to provide security in these devices. In this paper, we have reviewed the recent lightweight cryptography algorithms. Next, it defines the open research challenges and recommendations based on the literature review.

关键词： software algorithms Memory management NIST software Encryption Batteries Internet of Things

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System Construction of Virtual Simulation Experimental Teaching platform under Network Security and Computer Technology

System Construction of Virtual Simulation Experimental Teach...

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Information Science and Education (ICISE-IE), International Conference on

作者： Li Zhang Guolan Liu Huan Wan Jiangxi Tourism&Commerce Vocational College Nanchang China

ISBN: (纸本)9781665438308

At present, there are deficiencies in network security experiment resources, experiment environment, experiment management and computer technology virtual simulation experiment teaching in China. This paper proposes to explore the virtual simulation experiment teaching platform of network security and computer technology, provide high-quality experimental resources for experiments, and further improve students' practical ability and innovation ability. The key of network security and computer technology lies in computer algorithm. Based on the analysis of virtual experiment teaching mode, this paper expounds the application of computer algorithm in experimental modeling. Computer algorithm is a virtual experiment platform system model including computer programming modeling and computer software modeling. The construction of virtual experiment platform model is inseparable from the help of computer algorithm. This paper mainly expounds the construction process of virtual simulation experiment teaching resources and introduces the sharing mode of teaching resources.

关键词： Analytical models Technological innovation Computational modeling Education software algorithms Network security Mathematical models

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Statistical Algorithm model of College English Teaching based on artificial Intelligence Technology

Statistical Algorithm model of College English Teaching base...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xin Qin JiangXi Institute Of Economic Administrators Nanchang Jiangxi China

ISBN: (纸本)9781665438308

This paper mainly introduces the principle, characteristics and practical application of artificial intelligence technology in college English teaching system. The system analysis platform of college English teaching is established by using artificial intelligence technology. The platform system is mainly divided into two parts. The first is the expert system model of teacher English assisted instruction based on expert system theory, which is combined with the generation and framework of data analysis. Artificial intelligence has been widely used in college English teaching. Artificial intelligence technology has the advantages of intelligence, data, networking, multimedia and so on. And teaching information collected through artificial intelligence technology mining at the same time, the data using statistical algorithms accurately grasp the students' learning status, to improve and enhance English software platform operation function, improve the operation speed, efficiently for statistical analysis of students' learning behavior, effectively will artificial intelligence technology and system construction depth fusion, promoting the development of digital college English teaching.

关键词： Training Text mining Systematics Databases Education software algorithms Learning (artificial intelligence)

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Accelerating Workload Processing with MPI for Porter’s Stemming Algorithm

Accelerating Workload Processing with MPI for Porter’s Stem...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Eduardo Colmenares Heng Wu Department of Computer Science Midwestern State University Wichita Falls TX USA Department of Computer Science West Virginia State University Institute West Virginia USA

ISBN: (纸本)9781665458429

Stemming is the process of reducing a word to the root of words known as a lemma. For example, a stemming algorithm reduces the words "likes" and "likely" to the stem "like". Stemming is a part of linguistic studies and is used in information retrieval and extraction; and plays a critical role in artificial intelligence (AI), especially in natural language processing (NLP). Computers are now equipped with multicore processors and many of the software solutions to a diversity of problems do not take advantage of all these cores to process information and accelerate the processing of workloads. This research presents an effort to accelerate workload processing through the use of Message Passing Interface (MPI) for Porter’s stemming algorithm.

关键词： Vocabulary Program processors Multicore processing Scientific computing software algorithms Hard disks Natural language processing

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Automate platform for Capturing and Counting ELISpot on 96-Well Plate

Automate platform for Capturing and Counting ELISpot on 96-W...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Chayatorn Kukiattikoon Naphatsorn Vongsoasup Nopdanai Ajavakom Gridsada Phanomchoeng Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781665427159

Diagnosis is an important process for doctors to analyze and advise treatment options for patients. ELI Spot is used as one of the diagnostic processes for the patient. This process is tedious and fatigue. Thus, developing diagnostic tool is important to reduce the workload of doctors. In this work, the automated platform is developed to acquire image from 96-well plate and analyze the number of ELIS pot. The platform consists of an IAI Tabletop, a USB microscope camera, and an adjustable light source panel. The control and analysis software are developed by C# language with the industrial standard image processing software MVtec Halcon. To meet the requirement, the simple software interface is also designed for providing a user-friendly experience. Then, the software can control the platform to acquire images and process them for the result. The ELISpot counting is done by the developed dynamic thresholding algorithm. The ELISpot counting algorithm is evaluated with the gold standard. The percentage of the algorithm correction is more than 80%

关键词： Optical filters Thresholding (Imaging) Heuristic algorithms software algorithms Medical services Cameras Universal Serial Bus

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A Better Approach to Track the Evolution of Static Code Warnings

A Better Approach to Track the Evolution of Static Code Warn...

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Junjie Li Concordia University Montreal Canada

ISBN: (纸本)9781665412193;9781665429757

Static bug detection tools help developers detect code problems. However, it is known that they remain underutilized due to various reasons. Recent advances to incorporate static bug detectors in modern software development workflows can better motivate developers to fix the reported warnings on the fly. In this paper, we study the effectiveness of the state-of-the-art (SOA) solution in tracking warnings by static bug detectors and propose a better solution based on our analysis of the insufficiencies of the SOA solution. In particular, we examined four large-scale open-source systems and crafted a data set of 3,452 static code warnings by two static bug detectors. We manually uncover the ground-truth evolution status of the selected warnings: persistent, resolved, or newly-introduced. Moreover, upon manual analysis, we identified the critical reasons behind the insufficiencies of the SOA matching algorithm. Finally, we propose a better approach to improve the tracking of static warnings over software development history. Our evaluation shows that our proposed approach provides a significant improvement in the precision of the tracking, i.e., from 66.9% to 90.0%.

关键词： Computer bugs software algorithms Detectors Tools History Task analysis software engineering

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Single and Double PQ Disturbances Classification Based on Stockwell Transform and Support Vector Machine

Single and Double PQ Disturbances Classification Based on St...

引用

Power Engineering Application (ICPEA), IEEE International Conference in

作者： Mohd Saiful Najib B. Ismail Marzuki Ahmad Farid Abidin Mohd Zamri Jusoh Dalina Johari Faculty of Electrical Engineering Universiti Teknologi MARA Dungun Malaysia Faculty of Electrical Engineering Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9781728185477

Power quality disturbances can cause electrical appliances to malfunction, upsetting users who need the full performance of their device. A current and voltage waveforms interruption in power quality disturbances phenomena is due to the increased usage of electronic equipment. The problems to cater are, it can be harmful to users as well as reduce life expectancy of electrical products. In order to overcome this problems, classification of power quality disturbance is needed in order to provide the accurate information before the correct mitigation technique can be taken. In this paper, detection and classification technique, Stockwell transform, and Support Vector Machine were employed to classify nine types power quality disturbances including a single and multiple disturbances. Features were extracted to obtain a solid input for the classification purpose. The performance of the technique was analyzed and discussed to prove the reliability and the persistency of the technique.

关键词： Power quality software algorithms Support vector machine classification Transforms Feature extraction Solids Mathematical model

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Text sentiment classification based on BP neural network

Text sentiment classification based on BP neural network

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software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Nanchang Cheng Wenchao Soong Kang Song National Broadcast Media Language Resources Monitoring & Research Center Communication University of China Beijing China

In a consumer-oriented market, people's opinions or comments will, directly or indirectly, have influence on other people's decisions. For example, as choosing goods or online services, positive opinions or comments can promote consumers' purchase. At the same time, negative comments can also reduce consumers' enthusiasm for buying. The BP neural network will be trained with a supervised learning method, and finally obtain the ability that can classify positive or negative text reviews. In this paper, the supervised training method is adopted to improve the operation speed of the whole model by 8-9 times after optimizing the data and structure. The accuracy rate, recall rate, and F1 value are around 87.5%, 86.7%, and 87.35% respectively.

关键词： Training Neural networks software algorithms Text categorization Supervised learning Data models Classification algorithms

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Unsupervised Learning For Refactoring Pattern Detection

Unsupervised Learning For Refactoring Pattern Detection

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Congress on Evolutionary Computation

作者： Paulo Roberto Farah Thainá Mariani Enrique A. da Roza Rogério C. Silva Silvia Regina Vergilio Federal University of Paraná (UFPR) Curitiba Brazil

software refactoring changes the structure of a program without modifying its external behavior, generally intending to improve software quality attributes. However, refactoring is a complex activity and, many times, a composition of refactorings is necessary. Besides, some code elements are refactored similarly, considering the kind and frequency of refactorings applied. Works in the refactoring literature usually investigate the impact and understanding of an individual refactoring, neglecting that developers have to apply more than one refactoring operation to reach their goals. There is a lack of studies to identify and characterize refactoring patterns. To fulfill this gap, this work explores the use of unsupervised learning, particularly cluster analysis, to group elements (Java classes) that are refactored similarly in software repositories. We used a total of 1435 projects and applied the K-Means algorithm to group classes that received the same refactoring with the same frequency. We obtained a set of seven clusters. Then, the main refactoring compositions associated with each cluster are analyzed to identify the corresponding pattern. Each pattern is described and also characterized using a set of metrics. The great majority of refactoring compositions include only one kind of refactoring, applied with low frequency. If we consider compositions including more than one type of refactorings, combinations of Extract Superclass and Pull Up Method are the most frequent.

关键词： Measurement Java software algorithms Clustering algorithms software quality Evolutionary computation Unsupervised learning

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