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Characterization of polyvinyl alcohol hydrogels as tissue-engineered cartilage scaffolds using a coupled finite element-optimization algorithm

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JOURNAL OF BIOMECHANICS 2020年第0期99卷 109525-000页

作者： Nazouri, Mohadeseh Seifzadeh, Alireza Masaeli, Elahe Islamic Azad Univ Najafabad Branch Dept Med Engn Najafabad Iran ACECR Royan Inst Biotechnol Cell Sci Res Ctr Dept Cellular Biotechnol Esfahan Iran Islamic Azad Univ Khomeinishahr Branch Dept Med Engn Khomeinishahr Iran

Mechanical strength along with high biocompatibility and water absorbing are among main characteristics of a desirable scaffold for cartilage tissue engineering. Having these properties, polyvinyl alcohol (PVA) can be a good option for constructing cartilage tissue engineering scaffolds. In this study, PVA hydrogel was produced by freeze-thaw crosslinking method, and its mechanical properties such as viscoelastic and hyperplastic behavior, which cannot be obtained analytically, was investigated with a coupled finite element (FE)-optimization algorithm and stress relaxation experimental data. To obtain isotropic hyper-viscoelastic constitutive parameters of PVA scaffolds, the Mooney-Rivlin and Neo-Hooke strain energy functions, in which shear and bulk moduli varies with time, were applied. Results showed that predicted mechanical responses of scaffolds by the Mooney-Rivlin model better fitted stress-relaxation experiments than those obtained by Neo-Hooke one. Also, the properties obtained from the finite element model, such as the bulk and the shear moduli, showed that, after successful in vitro and in vivo experiments, PVA hydrogel may be introduced as a cartilage substitute for future tissue engineering therapies. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cartilage Polyvinyl alcohol optimization algorithm Finite Element

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An Agglomerative Greedy Brain Storm optimization algorithm for Solving the TSP

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IEEE ACCESS 2020年 8卷 201606-201621页

作者： Wu, Changyou Fu, Xisong Shandong Inst Business & Technol Sch Management Sci & Engn Yantai 264005 Peoples R China

The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.

关键词： Brain storm optimization algorithm traveling salesman problem hierarchical clustering optimization algorithm combinatorial optimization

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Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems

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ADVANCES IN ENGINEERING SOFTWARE 2020年 146卷 102804-102804页

作者： Das, Bikash Mukherjee, V. Das, Debapriya Govt Coll Engn & Textile Technol Dept Elect Engn Berhampur W Bengal India Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad Bihar India Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India

In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying to give more effort to improve their performance in the examination up to the level for becoming the best student in the class. Performance of the proposed SPBO is analyzed while applying the algorithm to solve thirteen 50 dimensional benchmark functions as well as fifteen CEC 2015 benchmark problems. Results of the SPBO is compared to the performance of ten other state-of-the-art optimization algorithms such as particle swarm optimization, teaching learning based optimization, cuckoo search algorithm, symbiotic organism search, covariant matrix adaptation with evolution strategy, success-history based adaptive differential evolution, grey wolf optimization, butterfly optimization algorithm, poor and rich optimization algorithm, and barnacles mating optimizer. For fair analysis, performances of all these algorithms are analyzed based on the optimum results obtained as well as based on convergence mobility of the objective function. Pairwise and multiple comparisons are performed to analyze the statistical performance of the proposed method. From this study, it may be established that the proposed SPBO works very well in all the studied test cases and it is able to obtain an optimum solution with faster convergence mobility.

关键词： Benchmark function CEC 2015 Global optimum solution optimization algorithm Student psychology based optimization (SPBO)

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An online optimization algorithm for the real-time quantum state tomography

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QUANTUM INFORMATION PROCESSING 2020年第10期19卷 361-361页

作者： Zhang, Kun Cong, Shuang Li, Kezhi Wang, Tao Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China UCL 222 Euston Rd London NW1 2DA England

Considering the presence of measurement noise in the continuous weak measurement process, the optimization problem of online quantum state tomography (QST) with corresponding constraints is formulated. Based on the online alternating direction multiplier method (OADM) and the continuous weak measurement (CWM), an online QST algorithm (QST-OADM) is designed and derived. Specifically, the online QST problem is divided into two subproblems about the quantum state and the measurement noise. The proposed algorithm adopts adaptive learning rate and reduces the computational complexity to O(d(3)), which provides a more efficient mechanism for real-time quantum state tomography. Compared with most existing algorithms of online QST based on CWM which require time-consuming iterations in each estimation, the proposed QST-OADM can exactly solve two subproblems at each sampling. The merits of the proposed algorithm are demonstrated in the numerical experiments of online QST for 1-, 2-, 3-, and 4-qubit systems.

关键词： Online quantum state tomography optimization algorithm Online alternating direction multiplier method Continuous weak measurement

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Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

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Procedia Computer Science 2020年 167卷 244-253页

作者： Pratibha Chaudhary Ritu Gupta Abhilasha Singh Pramathesh Majumder Ayushi Pandey

In this research, a joint image compression and encryption scheme is presented which works for different image dimensions 256X256, 512X512 and 1024X1024 and different image sizes. The proposed scheme is efficient as compression and encryption is done simultaneously which makes computations faster and secure at the same time. The compression scheme deployed is like JPEG compression but uses column-wise scanning and optimization instead of zigzag scanning. In entropy phase coding, Huffman coding of JPEG compression is replaced with Arithmetic coding. Image encrypted using XOR encryption is unbreakable, as used earlier in one-time pad. Compressed data is XORed with randomly generated number. The proposed scheme is implemented for grayscale images using MATLAB software version 2016a. The experimental results showed that the scheme provides good compression ratio, secures against attacks and takes less computation time. This can be further extended for RGB images of different dimension 256X256, 512X512 and 1024X1024 and different image sizes.

关键词： Column-wise scanning optimization algorithm DCT (Discrete cosine transform) Joint Compression Encryption JPEG Compression Quantization XOR Encryption

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Root Based optimization algorithm for Task-Oriented Structural Design of a Multi-Axial Road Test Rig

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IEEE ACCESS 2020年 8卷 168061-168078页

作者： Wang, Xiao Cong, Dacheng Yang, Zhidong Han, Junwei Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

A novel root based optimization algorithm (ROA), which mimics the proliferation of plant roots, is proposed to evaluate the performances of a multi-axial road test rig over a prescribed workspace. Plant roots have developed complex behavior patterns to search for nutrients in the soil and even exhibit swarm intelligence. The growing roots can adapt their actions such as elongation, branching, and tropistic movement according to the environment. During the process of foraging for water and nutrients, the adaptive growth behavior of roots can be simulated to tackle the optimization problem. The efficiency of the proposed ROA has been validated by comparing it with well-known intelligent algorithms using classical and IEEE CEC 2019 benchmark functions. Computational results indicate that ROA outperforms other algorithms to search a global optimum on several benchmarks. Furthermore, the results are tested with nonparametric statistical methods, e.g., Wilcoxon rank-sum test. Besides, ROA is applied to the optimal dimensioning synthesis of the multi-axial road test rig (MRTR) based on the orthogonal 6-RSS parallel manipulator to promote the device's performance. The inverse kinematics of the multi-axial road test rig based on the asymmetrical orthogonal 6-RSS parallel mechanism is analyzed in detail. Applying the achieved dimensionless Jacobian, the performance criteria involving the global isotropy index, the force/velocity transmissibility index, the decoupling index are established. By simultaneously taking account the proposed criteria of performance, structural optimization is carried out resorting to ROA. Finally, a set of optimized dimensional parameters is obtained. The practical application further illustrates that ROA has the potential to deal with complex optimization problems.

关键词： optimization Roads Particle swarm optimization Indexes Benchmark testing Kinematics Space exploration Root adaptive growth optimization algorithm benchmark test orthogonal 6-RSS parallel manipulator task-oriented performance criteria optimal structural design

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Optimized Wavelet-Based Satellite Image De-Noising With Multi-Population Differential Evolution-Assisted Harris Hawks optimization algorithm

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IEEE ACCESS 2020年 8卷 133076-133085页

作者： Golilarz, Noorbakhsh Amiri Mirmozaffari, Mirpouya Gashteroodkhani, Tayyebeh Asgari Ali, Liaqat Dolatsara, Hamidreza Ahady Boskabadi, Azam Yazdi, Mohammad Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA Univ Guilan Dept Elect Engn Rasht *** Iran Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Clark Univ Sch Management Worcester MA 01610 USA Washington State Univ Carson Coll Business Dept Finance & Management Sci Pullman WA 99163 USA Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

In this research, we propose to utilize the newly introduced Multi-population differential evolution-assisted Harris Hawks optimization algorithm (CMDHHO) in the optimization process for satellite image denoising in the wavelet domain. This optimization algorithm is the improved version of the previous HHO algorithm which consists of chaos, multi-population, and differential evolution strategies. In this study, we applied several optimization algorithms in the optimization procedure and we compared the de-noising results with CMDHHO based noise suppression as well as with the Thresholding Neural Network (TNN) approaches. It is observed that applying the CMDHHO algorithm provides us with better qualitative and quantitative results comparing with other optimized and TNN based noise removal techniques. In addition to the quality and quantity improvement, this method is computationally efficient and improves the processing time. Based on the experimental analysis, optimized based noise suppression performs better than TNN based image de-noising. Peak Signal to Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate and measure the performance of different de-noising methods. Experimental results indicate the superiority of the proposed CMDHHO based satellite image de-noising over other available approaches in the literature.

关键词： optimization Image denoising Wavelet transforms Signal processing algorithms Noise reduction Wavelet domain Thresholding (Imaging) CMDHHO optimization algorithm satellite image de-noising TNN wavelet domain

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An optimization algorithm to Design Compact Plate Heat Exchangers for Waste Heat Recovery Applications in High Power Datacenter Racks 18

An Optimization Algorithm to Design Compact Plate Heat Excha...

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18th IEEE InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm)

作者： Amalfi, Raffaele L. Cataldo, Filippo Thome, John R. Nokia Bell Labs Emerging Mat Components & Devices Lab 600 Mt Ave Murray Hill NJ 07974 USA Provides Metalmeccan Srl Via Piave 82 I-04100 Latina Italy JJ Cooling Innovat Sari Lausanne Switzerland LTCM EPFL Lausanne Switzerland

ISBN: (纸本)9781728124612

Heat dissipations of servers in datacenter racks are following an ever-increasing trend, breaking the economical heat removal limits of traditional air-based cooling technologies. Currently, an average of 40-45% of the total datacenter energy consumption is needed to cool servers, presenting significant challenges to maintain energy efficiencies and also noise levels within US OSHA standards The present paper focuses on the determination of the optimal design of a compact plate heat exchanger (PHE), acting as an overhead refrigerant-to-water condenser of a macro-scale thermosyphon, which dissipates the total heat from a datacenter rack into a cooling loop for waste heat recovery applications (e.g. district heating network). PHEs are already the preferred solution for many industrial and domestic applications (especially small to medium size refrigeration and heat pump systems), since they provide higher heat transfer performance, higher flexibility toward the targeted application and lower pressure drops compared to the conventional tube-in-tube and shell-and-tube heat exchangers. Furthermore, due to the numerous variables involved in the design of PHEs, such as plate number, plate footprint size, geometry of the corrugation pattern (i.e. chevron angle, pressing depth, etc.), an optimization analysis and corresponding simulation tool is auspicious to finding the optimal design of these units to accommodate the targeted heat rates of datacenter racks. Hence, this study proposes a novel optimization process which incorporates a local simulator (an improved version compared to the one presented at ITHERM 2018) for accurately rating and designing PHEs over a wide range of operating conditions, plate geometries and working fluids. The improved simulator uses a local one-dimensional effectiveness-NTU approach, with a local implementation of mass, momentum and energy equations, coupled with newly upgraded methods for condensation heat transfer coefficients and frictional p

关键词： Condenser datacenter rack optimization algorithm performance analysis plate heat exchanger two-phase flow

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Membrane System-based optimization algorithm for Numeric optimization Problem

Membrane System-based Optimization Algorithm for Numeric Opt...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Liu, Chuang Shen, Wanghui Du, Yingkui Lei, Jiahao Li, Ao Shenyang Univ Sch Informat Engn Shenyang 110044 Liaoning Peoples R China

ISBN: (纸本)9781728124858

In this paper, an optimization algorithm based on membrane system is proposed for numerical optimization problems. In the proposed algorithm, we designed two mechanisms to simulate the movement of molecules in arbitrary direction and a certain direction to balance global exploration and local exploitation. To test the performance of the proposed algorithm, eight benchmark functions were chosen. The simulation results show that the proposed algorithm is more advantageous than other experimental algorithms in solving numerical optimization problems.

关键词： optimization algorithm membrane systems numeric optimization problems

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DeP-D: A Decentralized Primal-Dual optimization algorithm for Industrial Wireless Sensor Networks 15

DeP-D: A Decentralized Primal-Dual Optimization Algorithm fo...

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15th IEEE International Workshop on Factory Communication Systems (WFCS)

作者： Farag, Hossam Gidlund, Mikael Osterberg, Patrik Mid Sweden Univ Dept Informat Syst & Technol Sundsvall Sweden

ISBN: (纸本)9781728112688

Industrial Wireless Sensor Networks (IWSNs) are emerged as flexible and cost-efficient alternatives to the traditional wired networks in various monitoring and control applications within the industrial domain. Low delay is a key feature of delay-sensitive applications as the data is typically valid for a short interval of time. If data arrives too late it is of limited use which may lead to performance drops or even system outages which can create significant economical losses. In this paper, we propose a decentralized optimization algorithm to minimize the End-to-End (E2E) delay of multi-hop IWSNs. Firstly, we formulate the optimization problem by considering the objective function as the network delay where the constraint is the stability criteria based on the total arrival rate and the total service rate. The objective function is proved to be strictly convex for the entire network, then a Decentralized Primal-Dual (DeP-D) algorithm is proposed based on the sub-gradient method to solve the formulated optimization problem. The performance of the proposed DeP-D is evaluated through simulations and compared with WirelessHART network and the results show that the proposed DeP-D can achieve at least 40% reduction in the average E2E delay.

关键词： Industrial wireless sensor networks end-to-end delay optimization algorithm

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