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Identifying Brand Consistency by Product Differentiation Using CNN

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 685-709页

作者： Hung-Hsiang wang chih-Ping chen Department of Industrial Design National Taipei University of TechnologyTaipeiTaiwan Department of Product Design Ming Chuan UniversityTaoyuanTaiwan

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance *** Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and *** show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various *** investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late *** show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real *** relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style *** addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred *** study provides insights into practical problems for designers,manufacturers,and marketers in product *** not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand *** can use these techniques to find features that influence brand ***,capture these features as innovative design elements and maintain core brand values.

关键词： Machine learning product differentiation brand consistency principal component analysis convolutional neural network computer mouse

chen-lu Tsou:A Pioneer of Biochemical Research in China

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Bulletin of the Chinese Academy of Sciences 2003年第2期17卷 118-124页

作者： chih-chen wang CAS Institute of Biophysics

Despite becoming an octogenarian this year, Prof. C. L. Tsou (Zou chenglu)still keeps strictly to a busy work schedule, coming to his laboratory at 8 am and leaving at 4 pmevery day. At weekends, he buries himself in literature, reading, pondering and writing. In short,work is the eternal theme and major part of his life. Recently, however, he took his doctor's adviceand added a walk to his daily routine. Prof. Tsou was born in 1923 in Wuxi City, Jiangsu *** the locals have a unique dialect, he speaks perfect Mandarin. He also speaks Shanghaidialect with an accent, which can be attributed to his work in Baoshan, a suburban county inShanghai in the mid-1960s. In 1941, after he graduated from the Nankai Middle School in Chongqing,the wartime capital of China, he was admitted to the National Southwest Associate University as achemistry major and graduated in 1945. Two years later, he went to England on a state scholarshipand came home in 1951, after obtaining his Ph.D. in biochemistry from Cambridge University. For 20years he worked at the Shanghai Institute of Biochemistry, a CAS affiliate, and in 1971 he moved tothe CAS Institute of Biophysics (IBP) in Beijing, where he personally guided the founding of theNational Laboratory for Biomacromolecules, working there till now. During the past 50 years, he usedto serve as a member of the CAS Presidium of Academic Divisions (CASAD), director of the AcademicDivision of Biology, and chairman of the executive council under Chinese Society of Biochemistry andMolecular Biology. In addition, he served from 1978 to 1997 on the 5^(th) to 8^(th) NationalCommittee of the Chinese People's Political Consultative Conference, the national top advisory bodyand also its 8th standing committee. In 1980, he was elected to CAS. He is a fellow of the ThirdWorld Academy of Sciences and an honorary member of the American Society for Biochemistry andMolecular Biology. For many years, he has sat as a member of the editorial board or

关键词：中国科学院生物化学家 Zou-cheng-lu 蛋白质胰岛素呼吸酶

Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots

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Science China(Information Sciences) 2024年第5期67卷 197-214页

作者： Yefeng YANG Tao HUANG Tianqi wang Wenyu YANG Han chen Boyang LI chih-yung WEN Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Center for Control Theory and Guidance Technology Harbin Institute of Technology School of Engineering The University of Newcastle Research Center for Unmanned Autonomous Systems The Hong Kong Polytechnic University

Autonomous robots have garnered extensive utilization in diverse fields. Among the critical concerns for autonomous systems, path planning holds paramount importance. Notwithstanding considerable efforts in its development over the years, path planning for autonomous systems continues to grapple with challenges related to low planning efficiency and inadequate obstacle avoidance response in a timely *** study proposes a novel and systematic solution to the path planning problem within intricate office buildings. The solution consists of a global planner and a local planner. To handle the global planning aspect, an adaptive clustering-based dynamic programming rapidly exploring random tree(ACDP-RRT) algorithm is proposed. ACDP-RRT effectively identifies obstacles on the map by leveraging geometric *** obstacles are then represented as a collection of sequentially arranged convex polygons, optimizing the sampling region and significantly enhancing sampling efficiency. For local planning, a network decoupling actor-critic(ND-AC) algorithm is employed. The proposed ND-AC simplifies the local planner design process by integrating planning and control loops into a neural network(NN) trained via an end-to-end model-free deep reinforcement learning(DRL) framework. Moreover, the adoption of network decoupling(ND) techniques leads to an improved obstacle avoidance success rate when compared to conventional actor-critic(AC)-based methods. Extensive simulations and experiments are conducted to demonstrate the effectiveness and robustness of the proposed approach.

关键词： rapidly exploring random tree (RRT) adaptive clustering network decoupling actor critic (AC) path planning

Subdomain-Specific Collapse of Denatured Staphylococcal Nuclease Revealed by Single Molecule Fluorescence Resonance Energy Transfer Measurements

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Subdomain-Specific Collapse of Denatured Staphylococcal Nucl...

Track correlation algorithm based on CNN-LSTM for swarm targets

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作者： chih-chen wang National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of Sciences15 Datun Road

＜正＞By using single molecule fluorescence resonance energy transfer（smFRET）,the equilibrium denaturation of staphylococcal nuclease（SNase） induced by guanidinium hydrochloride（GdmCl） has been *** have characterized the collapse of the denatured chain and its relation to structure *** mutants,K28C/H124C and K28C/K97C,were constructed and labeled for

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Journal of Systems Engineering and Electronics 2024年第2期35卷 417-429页

作者： chen Jinyang wang Xuhua chen Xian Research Institute for National Defense Engineering Academy of Military SciencesLuoyang 471023China Information Engineering University Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi’an 710071China

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for *** this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm ***, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

关键词： track correlation correlation accuracy rate swarm target convolutional neural network(CNN) long short-term memory(LSTM)neural network

“后AlphaFold时代”的蛋白质折叠问题

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科学通报 2023年第22期68卷 2943-2950页

作者：胡昕炜王志珍王磊中国科学院生物物理研究所生物大分子国家重点实验室北京100101 中国科学院大学生命科学学院北京100049

以AlphaFold为代表的使用深度学习人工智能的蛋白质结构预测方法改变了结构生物学乃至整个生命科学领域.生命科学的研究已进入“后AlphaFold时代”,但我们对细胞环境内的蛋白质折叠过程还知之甚少.在本文中,我们回顾了蛋白质折叠早期的... 详细信息

以AlphaFold为代表的使用深度学习人工智能的蛋白质结构预测方法改变了结构生物学乃至整个生命科学领域.生命科学的研究已进入“后AlphaFold时代”,但我们对细胞环境内的蛋白质折叠过程还知之甚少.在本文中,我们回顾了蛋白质折叠早期的研究历史.

关键词：深度学习蛋白质折叠结构生物学人工智能生命科学蛋白质结构预测 Alpha 时代

Toward Artificial Peptide Nanocapsules

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Nano-Micro Letters 2024年第10期16卷 240-243页

作者： Yuan wang Bing chen Laboratory for Biomaterials and Drug Delivery Department of AnesthesiologyDivision of Critical Care MedicineBoston Children's HospitalHarvard Medical SchoolBostonMA 02115USA College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023People's Republic of China

HIGHLIGHTS The formation of peptide nanocapsules is facilitated by a gradient interface,where the differential solvent concentration drives the peptides to preferentially localize and *** peptide nanocapsules,characterized by their hollow structures,demonstrated potential as carriers for targeted drug delivery.1 Introduction Peptide nanocapsules are a type of nanoscale delivery system that encapsulates active substances within a shell composed of peptides,leveraging the unique properties of peptides such as biocompatibility and biodegradability[1].Historically,the development of peptide nanocapsules was inspired primordially by the natural biological processes.

关键词： Peptide nanocapsules Self-assembly Drug delivery Nanomedicine

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

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Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 603-618页

作者： chen AN Jiaxi ZHOU Kai wang College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHong KongChina

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter *** hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external ***,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial ***,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system ***,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and *** simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

关键词： aerial robot hierarchical control strategy model-free iterative sliding mode controller(MFISMC) trajectory tracking reinforcement learning

Evaluation of 3D crustal seismic velocity models in southwest China:Model performance,limitation,and prospects

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Science China Earth Sciences 2024年第2期67卷 604-619页

作者： Xin wang Ling chen Qi-Fu chen Key Laboratory of Earth and Planetary Physics Institute of Geology and GeophysicsChinese Academy of SciencesBeijing100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing100049China State Key Laboratory of Lithospheric Evolution Institute of Geology and GeophysicsChinese Academy of SciencesBeijing100029China

Southwest China is a tectonically and seismically active region,witnessing strong deformation due to the collision between the Indian and Eurasian *** the subsurface velocity structure of this region is thus important in understanding the tectonics and geodynamic processes of continental collision and in mitigating seismic *** studies have provided various 3D seismic velocity models in southwest ***,discrepancies exist among these models,and less effort has been made to quantify the reliability and accuracy of these existing velocity *** this study,we use regional 3D waveform simulation to evaluate the performance of various regional crustal 3D velocity models in reproducing observed *** particularly focus on two recent earthquake sequence in the region,the 2021 Yunnan Yangbi MS_(6.4) earthquake sequence and the 2022 Sichuan Luding MS_(6.8) earthquake *** tested 3D velocity models include the Southwest China Community Velocity model V1.0,the Unified Seismic Tomography Models for Continental China Lithosphere V2.0,the adjoint full waveform tomography model of the crustal and upper mantle beneath Eastern Tibetan Plateau,and the shallow seismic structure model beneath continental *** results show that the tested 3D velocity models generally capture well long-period(<0.2 Hz)waveforms,indicating that the 3D models adequately resolve overall large-scale subsurface ***,the 3D synthetics show discrepancies in higher frequencies(0.05–0.3 Hz)and the performance of the 3D velocity models varies from region to region,suggesting that smaller scale heterogeneities are not well *** shallow velocity structures(<10 km)can improve the waveform fitting,emphasizing the importance of incorporating shallow structures in waveform *** full-waveform tomography model shows a slighter better performance than the other models,especially for the body-waves,highlighting the advantages

关键词： Southwest China Seismic tomography models Shallow Structures Waveform simulation Model evaluation