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8th International Symposium on Computational Intelligence and Design (ISCID)

作者： Zeng, Yu Bi, Yuan Wang, Jie Lin, Yun Peking Univ Sch Econ Beijing 100871 Peoples R China Capital Normal Univ Sch Management Beijing 100089 Peoples R China

ISBN: (纸本)9781467395878

Aiming at the data sparse and cold start problems in collaborative filtering recommendation algorithm, an optimized solution based on user characteristics and user ratings is proposed in this paper. Based on users' basic attributes and users' history score record, the similarity of users and the similarity of items are calculated, and the nearest neighbor users and similar items are obtained. The advantage of the algorithm is that it combines the user's score and personal attributes to calculate the similarity between users and to recommend items. The optimized algorithm is applied to the recommendation of insurance products. Experiments based on real data from insurance company show that this method can reduce the average absolute error and improve the accuracy of recommendation.

关键词： user attributes collaborative filtering recommendation Insurance products algorithm optimization

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Bit Rate Matching algorithm optimization in JPEG-AI Verification Model

Bit Rate Matching Algorithm Optimization in JPEG-AI Verifica...

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Picture Coding Symposium (PCS)

作者： Jia, Panqi Koyuncu, A. Burakhan Mao, Jue Cui, Ze Ma, Yi Guo, Tiansheng Solovyev, Timofey Karabutov, Alexander Zhao, Yin Wang, Jing Alshina, Elena Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Erlangen Germany Tech Univ Munich Chair Media Technol Munich Germany Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9798350358483;9798350358490

The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit representations, and achieve faster coding speed on parallel devices over their classical counterparts. Those properties evoked the attention of both scientific and industrial communities, resulting in the standardization activity JPEG-AI. The verification model for the standardization process of JPEG-AI is already in development and has surpassed the advanced VVC intra codec. To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching, resulting in suboptimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold.

关键词： Learned Image Compression JPEG-AI bit rate matching algorithm optimization

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Spacecraft design using a genetic algorithm optimization approach

Spacecraft design using a genetic algorithm optimization app...

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IEEE Aerospace Conference

作者： Mosher, T Univ Colorado Redondo Beach CA 90278 USA

ISBN: (纸本)0780343115

Spacecraft design is a highly coupled problem. The design of the spacecraft must balance payload objectives and orbital design against cost and schedule guidelines. Currently spacecraft are 'optimized' manually through a tool-assisted evaluation of a limited set of design alternatives. With this approach there is no guarantee that a system-level focus will be taken and 'feasibility' rather than 'optimality' is commonly all that is achieved.

关键词： Spacecraft Set design spacecraft design algorithm optimization cost analysis utility tool NEAR EARTH ASTEROIDS optimality Genetic algorithm (GA)

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Research on the Evaluation of Chinese Articulation Using Machine Learning and algorithm optimization 3

Research on the Evaluation of Chinese Articulation Using Mac...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Tang, Meng Sichuan Technology & Business College Chengdu China

ISBN: (纸本)9798350380989

This design uses SPCE061A, SPLC501 and keyboard module to realize speech recognition system. The system realizes the functions of adding, deleting and searching basic information. The data saved in SPR4096 was recognized by voice. A steady state interference noise for Chinese speech audibility detection based on pink noise is proposed, and its frequency is consistent with the speech features recorded in anechoic chamber. The corresponding training samples were obtained by convolution of the Chinese speech audibility test word list with the equivalent binaural chamber impulse response. It is then tuned to a specific signal strength. The objective evaluation of the intelligibility of Chinese speech is done by audiologists. It is found that factors such as reverberation time, signal-to-noise ratio and pronunciation speed play an important role in the intelligibility of Chinese speech. Compared with reverberation time and speech frequency, SNR plays a more significant role in the intelligibility of Chinese speech. Compared with conventional methods, this method has better recognition performance and speed. © 2024 IEEE.

关键词： algorithm optimization articulation rate Chinese language articulation Chinese pronunciation Machine learning

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ANKLE JOINT MOTION RECOGNITION SYSTEM AND algorithm optimization BASED ON PLANTAR PRESSURE

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2024年第10期24卷

作者： Lu, Zongxing Xu, Zhiwei Zhao, Dongzhe Yang, Tianxue Fuzhou Univ Sch Adv Mfg 1 Shuicheng Rd Jinjiang 362200 Peoples R China Fuzhou Univ Sch Mech Engn & Automat 2 Xueyuan Rd Fuzhou 350116 Peoples R China Fujian Special Equipment Inspection & Res Inst Fujian Key Lab Special Intelligent Equipment Safet Fuzhou 350008 Peoples R China

Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human-machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients' ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.

关键词： Plantar pressure acquisition system motion recognition data processing algorithm optimization

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Architecture-based and target-oriented algorithm optimization of high-order methods via complete-search tensor contraction

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COMPUTER PHYSICS COMMUNICATIONS 2021年 264卷 107988-107988页

作者： You, Hojun Kim, Chongam Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea

Sophisticated solution algorithms, along with complex data structures, are known as the main barriers that hinder high-order methods from being actively embraced by industry and academia. Simultaneously, modern computing machines offer a wide variety of opportunities to enhance the performance of solution algorithms through highly tuned computational kernels. To address this issue, we present an architecture-based and target-oriented algorithm optimization for high-order methods, called completesearch tensor contraction (CsTC). The key idea of CsTC is to convert the tensor operations of a high-order method into an optimization problem, which leads to finding an optimized method to execute tensor contraction (TC). After introducing the general framework of CsTC, it was applied to the discontinuous Galerkin (DG) discretization. An approach based on general matrix multiplication (GEMM) is adopted because of its flexibility to handle the intermediate order of TC and the reusability of state-of-the-art GEMM primitives. By optimizing data structures as well as TC operations, CsTC provides an optimized solution algorithm that performs significantly better than the original non-optimized high-order method. The entire optimization process is automatically completed in a few minutes at a pre-processing step on a computer. The proposed CsTC optimization fully reflects the mesh and solution parameters adopted as well as the computing architecture used, thus, it is completely target-oriented and architecture-based. Various solution parameters and computing architectures are used and compared. All the results indicate that the optimization is essential to extract the best performance from a given computing architecture and that the performance enhancement becomes substantial as the DG approximation order increases and as a more recent processor is employed. Finally, a 3-D viscous flow problem governed by the compressible Navier-Stokes equations is solved. The optimized algorithm

关键词： Tensor contraction algorithm optimization General matrix multiplication Discontinuous Galerkin method Navier-Stokes equations

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Research on Ant Colony algorithm optimization Neural Network Weights Blind Equalization algorithm

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第2期10卷 95-104页

作者： Geng, Yanxiang Zhang, Liyi Sun, Yunshan Zhang, Yao Yang, Nan Wu, Jiawei Tianjin Univ Commerce Coll Informat & Engn Tianjin 300134 Peoples R China Tianjin Univ Coll Informat & Engn Tianjin 300072 Peoples R China

The project of ant colony algorithm optimization neural network combining blind equalization algorithm is proposed. The better initial weights of neural networks are provided because of the randomness, ergodicity and positive feedback of the ant colony algorithm. And then, a combination of optimal weights are found through BP algorithm, which is fast local search speed. Thus blind equalization performance is improved. Computer simulation show that, the novel blind equalization algorithm speeds up the convergence rate, reduces the remaining steady-state error and bit error rate, which is compared with the Neural Network Blind Equalization algorithm(NNBE) and Genetic algorithm optimization Neural Network Blind Equalization algorithm(GA-NNBE).

关键词： Ant colony algorithm Neural network Blind equalization Initial weight algorithm optimization

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Genetic algorithm optimization for Codewords Correction in MIMO Broadcast Channels

Genetic Algorithm Optimization for Codewords Correction in M...

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IEEE International Midwest Symposium on Circuits and Systems

作者： Mouncef Benmimoune Daniel Massicotte Sebastien Roy Electrical and Computer Engineering Department Universite du Quebec a Trois-Rivieres Laval University

ISBN: (纸本)9781467325264

This paper presents a novel precoding approach for MIMO broadcast channels, in which is performed on both sides of the wireless link. The aim of the proposed approach is to avoid the same precoding vectors choice when all users use a common codebook. In the proposal approach, firstly, we focus on the brute codeword selection at the transmitter side, thus there is zero probability to choose the same vector for more than one user. However, this solution leads an exhaustive search, especially when the number of user and the codebook size increase. To overcome this issue, secondly, we adopt the genetic algorithm in order to reduce the codeword search complexity. Compared with zero-forcing beamforming (ZFBF), the conducted simulation results for the critical scenario of low SNR, show that our scheme is better than ZFBF with the assumption of both perfect and partial channel state information at the transmitter.

关键词： broadcast channels algorithm optimization Genetic algorithm (GA) Channel state information multiple input multiple output systems Transmitters codeword codebook low SNR prediction coding exhaustive search Consumers Transmitter

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Optimized federated learning based on Adagrad algorithm and algorithm optimization

Optimized federated learning based on Adagrad algorithm and ...

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作者： Enming Chu Dengbo Li Yangfan Tong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications School of Computing and Data Science Xiamen University Malaysia

Federated learning allows you to train machine learning models without sharing your local *** to the No-iid problem,this paper is based on the Moon algorithm,which can have excellent performance in datasets of images with models that use deep learning and outperforms FedAvg,FedProx,and other algorithms,with the goal to decrease communication costs while enhancing efficiency more *** study optimizes its gradient descent technique based on Moon's algorithm by utilizing Adaptive Gradient(AdaGrad) optimizer and combining with knowledge distillation to improve Moon's algorithm in order to better reduce communication costs and improve *** is,it reduces the loss and improves the accuracy faster and better in local *** this paper,we experimentally show that the optimized moon can better solve the communication cost and improve the accuracy rate.

关键词： AdaGrad moon federated learning algorithm optimization

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Simulation of enterprise performance evaluation data indicators based on improved neural network algorithm and parameter optimization

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SOFT COMPUTING 2023年 1-12页

作者： Ma, Fushan Shao, Chunhui Lianyungang Normal Coll Lianyungang Jiangsu Peoples R China Lianyungang Higher Vocat & Tech Sch Ind & Trade Ji Lianyungang Jiangsu Peoples R China

The amount of data required in enterprise performance evaluation is increasing, and the number and complexity of data indicators are also increasing. Traditional enterprise performance evaluation methods often need a lot of manpower, material and financial input, and the accuracy and reliability of the results are difficult to guarantee. How to use computer science and technology to realize the intelligence and precision of enterprise performance evaluation becomes an important research object. This paper takes enterprise performance evaluation data index simulation as the research object, aiming to explore a new method to realize the precision and intelligence of enterprise performance evaluation. The improved neural network algorithm and parameter optimization technology are used to simulate the relevant data indicators of enterprise performance evaluation, so as to realize the intelligent analysis and accurate prediction of enterprise performance evaluation. The results show that the improved neural network algorithm and parameter optimization technology proposed in this paper can effectively simulate and predict the data indicators of enterprise performance evaluation.

关键词： BP neural network algorithm optimization Enterprise performance Network parameters

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