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Structural analysis and Application of Non-Standard Components Based on Genetic algorithm

algorithmS 2019年第8期12卷 169-169页

作者： Lei, Zhao Lai, Hu Hua, Zhang Hua, Chen Chongqing Vocat & Tech Univ Mechatron Sch Elect & Elect Engn Chongqing 402760 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Aiming at the problems of low efficiency, heavy quality, and high cost of traditional components, it is necessary to study a design and analysis method of non-standard components. Taking the non-standard parts-turret loading and -unloading device as the carrier, the key parts of the non-standard parts are extracted for structural design and the multi-objective mathematical model and modal theory model are established. The optimization analysis of the key parts is carried out by genetic algorithm. Finally, the optimization results are compared and simulated by ANSYS Workbench. The results show that: in this case, the genetic algorithm optimized data with other data, the overall quality difference is 4.1%. The first six order modal values in the optimized results are in the range of 68 Hz to 130 Hz, which provides a basis for similar research in the future.

关键词： non-standard parts algorithm analysis multi-objective optimization analysis

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Investigation of fiber maturity measurement by cross-sectional image analysis and Fourier transform infrared spectroscopy on developing and developed upland cottons

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CELLULOSE 2019年第10期26卷 5865-5875页

作者： Liu, Yongliang Kim, Hee-Jin Delhom, Christopher D. Thibodeaux, Devron P. ARS SRRC Cotton Struct & Qual Res Unit USDA New Orleans LA 70124 USA ARS SRRC Cotton Fiber Biosci Res Unit USDA New Orleans LA 70124 USA Fiber Phys LLC Metairie LA 70005 USA

Cotton fiber maturity is an important fiber physical and quality property that impacts downstream fiber processing. Fiber maturity refers to the degree of secondary cell wall thickening. The reference method for fiber maturity measurement is to quantify the secondary cell wall area relative to the perimeter of the fiber via cross-sectional image analysis, a tedious and slow process. A number of approaches have been developed which attempt to measure fiber maturity rapidly. The approach employed in this work is based on the use of attenuated total reflection Fourier transform infrared spectroscopy, and simple algorithms were developed from the spectra to estimate fiber maturity directly. To validate the efficacy of this approach against cross-sectional image analysis, two distinct fiber sets were examined that included a set of developing fibers and a diverse set of developed fibers. Comparison of image analysis and infrared maturity results imply a consistency and equivalency between the two maturity measurements.

关键词： Fourier transform infrared spectroscopy Attenuated total reflection Cotton fiber Cellulose Maturity algorithm analysis

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Locality Sensitive Hashing Revisited: Filling the Gap Between Theory and algorithm analysis 13

Locality Sensitive Hashing Revisited: Filling the Gap Betwee...

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22nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Hongya Cao, Jiao Shu, Lihchyun Rafiei, Davood Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Natl Cheng Kung Univ Coll Management Tainan Taiwan Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (纸本)9781450322638

Locality Sensitive Hashing (LSH) is widely recognized as one of the most. promising approaches to similarity search in high-dimensional spaces. Based on LSH, a considerable number of nearest neighbor search algorithms have been proposed in the past, with some of them having been used in many real-life applications. Apart from their demonstrated superior performance in practice, the popularity of the LSH algorithms is mainly due to their provable performance bounds on query cost, space consumption and failure probability. In this paper, we show that a surprising gap exists between the LSH theory and widely practiced algorithm analysis techniques. In particular, we discover that a critical assumption made in the classical LSH algorithm analysis does not hold in practice, which suggests that using the existing methods to analyze the performance of practical LSH algorithms is a conceptual mismatch. To address this problem, a novel analysis model is developed that bridges the gap between the LSH theory and the method for analyzing the LSH algorithm performance. With the help of this model, we identify some important flaws in the commonly used analysis methods in the LSH literature. The validity of this model is verified through extensive experiments with real datasets.

关键词： Locality Sensitive Hashing Probabilistic algorithm algorithm analysis

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Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics

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APPLIED SOFT COMPUTING 2021年 109卷 107492-107492页

作者： Ochoa, Gabriela Malan, Katherine M. Blum, Christian Univ Stirling Comp Sci & Math Stirling Scotland Univ South Africa Dept Decis Sci Pretoria South Africa Artificial Intelligence Res Inst IIIA CSIC Bellaterra Spain

A large number of metaheuristics inspired by natural and social phenomena have been proposed in the last few decades, each trying to be more powerful and innovative than others. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of metaheuristics when solving optimisation problems. When the metaphors are stripped away, are these algorithms different in their behaviour? To help to answer this question, we propose a data-driven, graph-based model, search trajectory networks (STNs) in order to analyse, visualise and directly contrast the behaviour of different types of metaheuristics. One strength of our approach is that it does not require any additional sampling or algorithmic methods. Instead, the models are constructed from data gathered while the metaheuristics are solving the optimisation problems. We present our methodology, and consider in detail two case studies covering both continuous and combinatorial optimisation. In terms of metaheuristics, our case studies cover the main current paradigms: evolutionary, swarm, and stochastic local search approaches. (c) 2021 Elsevier B.V. All rights reserved. Superscript/Subscript Available

关键词： algorithm analysis Search trajectories Complex networks Continuous optimisation Combinatorial optimisation Visualisation

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TKFIM: Top-K frequent itemset mining technique based on equivalence classes

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PEERJ COMPUTER SCIENCE 2021年 7卷 e385页

作者： Iqbal, Saood Shahid, Abdul Roman, Muhammad Khan, Zahid Al-Otaibi, Shaha Yu, Lisu Kohat Univ Sci & Technol Inst Comp Kohat Kpk Pakistan Prince Sultan Univ Robot & Internet Things Lab Riyadh Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh Saudi Arabia Nanchang Univ Sch Informat Engn Nanchang Jiangxi Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs;thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

关键词： Frequent Itemsets Support Threshold algorithm analysis Top-k Frequent Itemsets Artifical Intelligence

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Development of an Image Data Set of Construction Machines for Deep Learning Object Detection

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2021年第2期35卷

作者： Xiao, Bo Kang, Shih-Chung Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2R3 Canada

Deep learning object detection algorithms have proven their capacity to identify a variety of objects from images and videos in near real-time speed. The construction industry can potentially benefit from this machine intelligence by linking algorithms with construction videos to automatically analyze productivity and monitor activities from a safety perspective. However, an effective image data set of construction machines for training deep learning object detection algorithms is not currently available due to the limited accessibility of construction images, the time-and-labor-intensiveness of manual annotations, and the knowledge base required in terms of both construction and deep learning. This research presents a case study on developing an image data set specifically for construction machines named the Alberta Construction Image Data Set (ACID). In the case of ACID, 10,000 images belonging to 10 types of construction machines are manually collected and annotated with machine types and their corresponding positions on the images. To validate the feasibility of ACID, we train the data set using four existing deep learning object detection algorithms, including YOLO-v3, Inception-SSD, R-FCN-ResNet101, and Faster-RCNN-ResNet101. The mean average precision (mAP) is 83.0% for Inception-SSD, 87.8% for YOLO-v3, 88.8% for R-FCN-ResNet101, and 89.2% for Faster-RCNN-ResNet101. The average detection speed of the four algorithms is 16.7 frames per second (fps), which satisfies the needs of most studies in the field of automation in construction.

关键词： Deep learning Object detection algorithm analysis Construction machines Image data set

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A Comparative Study on k-means Clustering Method and analysis 1

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2nd International Conference on Emerging Technologies in Computer Engineering - Microservices in Big Data Analytics (ICETCE)

作者： Baruri, Rajdeep Ghosh, Anannya Chanda, Saikat Banerjee, Ranjan Das, Anindya Mandal, Arindam Halder, Tapas Jadavpur Univ Dept Comp Sci & Engn Kolkata India Inst Engn & Management Dept Comp Sci & Engn Kolkata India Kolkata Police Cyber Patrol Cell Kolkata India

ISBN: (数字)9789811383007

ISBN: (纸本)9789811383007;9789811382994

A study of three clustering methods using four different cluster validity metrics is being presented here. We have discussed the clustering methods and made an analysis. We have given the mathematical formation of four cluster validity measures. From the experimental outcomes, indications regarding the optimal validation method, as well as, optimal clustering method are being presented. Choice of preferable clustering technique is presented after getting outcomes using real-world data sets.

关键词： Data analytics Machine learning algorithm analysis Clustering validity

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Visual analysis of Differential Evolution algorithms 11th

Visual Analysis of Differential Evolution Algorithms

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11th International Conference on Multimedia and Network Information Systems (MISSI)

作者： Mexicano-Santoyo, A. Rodriguez-Jorge, R. Abrego, A. Jimenez, M. A. Zuniga-Trevino, R. Martinez-Garcia, Edgar A. Inst Tecnol Cd Victoria Mexico Univ Autonoma Ciudad Juarez Inst Engn & Technol Ciudad Juarez 32310 Chihuahua Mexico Univ Autonoma Tamaulipas Cd Victoria Mexico

ISBN: (纸本)9783319986784;9783319986777

In this article a web tool which contributes to the visual analysis of the Differential Evolution (DE) algorithms is presented. The tool provides a graphic interface with 8 views that allows understanding the underlying process of the algorithm. The tool has a library which extracts data from DE algorithms and its main feature is that the functions of the library can be embedded in the code of any DE algorithm to be analyzed. To validate the tool, three DE algorithms: DE/Rand/1/bin, DE/best/1/bin, and JADE and three test functions: Sphere, Rosenbrock, and Rastrigin have been used, which produced a total of 234 different tests, all of them performed successfully. The tool can allow to experts to analyze algorithms, particularly DE algorithms, and it can contribute to improve such algorithms or in generating new strategies that can emerge from the analysis of the extracted information.

关键词： Differential evolution algorithms algorithm analysis Web tool

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Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2019年第12期30卷 2677-2691页

作者： Xia, Qiufen Xu, Zichuan Liang, Weifa Yu, Shui Guo, Song Zomaya, Albert Y. Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Int Sch Informat Sci & Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Liaoning Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Univ Technol Sydney Sch Software Ultimo NSW 2007 Australia Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Univ Sydney Sch Comp Sci Camperdown NSW 2006 Australia

Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the proposed algorithms are promisi

关键词： Big Data Query processing Delays Approximation algorithms Quality of service Distributed databases Software Data replication and placement big data analytics approximate query evaluation approximation algorithms algorithm analysis

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Comparative Investigation of Secondary Cell Wall Development in Cotton Fiber Near Isogenic Lines Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR FT-IR)

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APPLIED SPECTROSCOPY 2019年第3期73卷 329-336页

作者： Liu, Yongliang Kim, Hee-Jin ARS USDA SRRC Cotton Struct & Qual Res Unit New Orleans LA USA ARS USDA SRRC Cotton Fiber Biosci Res Unit New Orleans LA USA

In this investigation, we applied previously proposed simple algorithms to analyze the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra of cotton fibers during secondary cell wall (SCW) biosynthesis. The infrared crystallinity (CIIR) and maturity (M-IR) indices were compared from developmental fibers representing two pairs of upland cotton near isogenic lines (NILs). One pair of NILs consisted of Texas Marker-1 (TM-1) and an immature fiber (im) mutant that differ in fiber maturity. The other pair of NILs included MD52ne and MD90ne that show variations in fiber strength. The observations revealed significant difference in the M-IR values between developmental TM-1 and im NILs grown at a field in crop year 2015, and also a significant difference in the CIIR values between these NILs grown at the same field in crop year 2011. These different patterns of CIIR and M-IR values during fiber development for the two different crop years indicated the impact of genetics and crop year on the development of fiber maturity and crystallinity of the TM-1 and im fibers. Furthermore, the tendency of linking CIIR with M-IR values suggested that the im fibers have more CIIR development than the TM-1 fibers when the fibers have the similar M-IR values. In contrast, the NIL pair having variations in fiber strength showed insignificant differences in the patterns of CIIR and M-IR as well as the relationship between CIIR and M-IR values. The results suggested that CIIR and M-IR indices from ATR FT-IR measurement could be used to facilitate the understanding of how fiber genetics and crop year affect fiber maturity and crystallinity during SCW biosynthesis.

关键词： Fourier transform infrared spectroscopy FT-IR attenuated total reflection ATR cotton near isogenic lines immature fiber im mutant fiber secondary cell wall biosynthesis algorithm analysis

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