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101.
In a highly competitive market, customers' product affection is a critical factor to product success. However, understanding customers' affective needs is difficult to grasp; product design practitioners often misunderstand what customers really want. In this study we report our experience in developing and using an affective design framework that identified critical affective features customers have on products and are systematically incorporated into product design attributes. To identify key affective features such as luxuriousness, we utilized the Kansei engineering methodology. This approach consists of three steps: (1) selecting related affective features and product design attributes through a comprehensive literature survey, expert panel opinion, and focus group interviews; (2) conducting evaluation experiments; and (3) developing Kansei models using multivariate statistical analysis and analyzing critical product design attributes. To demonstrate applicability of the proposed affective design framework, 30 customers and 30 product design practitioners participated in an evaluation experiment for car crash pads, and 44 customers and 20 designers participated in an evaluation experiment for two interior room products (wallpapers and flooring materials). The evaluation experiments were conducted via systematically developed questionnaires consisting of a 7‐point semantic differential scale and a 100‐point magnitude estimation scale. The results of the experiments were analyzed using principal component regression and quantification theory type I method. Using the analyzed survey data, the relationship between luxuriousness and related affective features and product design attributes were identified. This relationship indicated that there was a significant difference in the perception of luxuriousness between customers and designers. Consequently, it is expected that the results of this study could provide a foundation for developing affective products. © 2009 Wiley Periodicals, Inc. 相似文献
102.
Sunil Kumar Richard J. Pippy Erdem Acar Nam H. Kim Raphael T. Haftka 《Structural and Multidisciplinary Optimization》2009,38(6):613-626
Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses).
The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is
usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In
previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure
via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based
on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the
change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact,
thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential
Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has
probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require
additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability
constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar
truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members.
It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic
optimization. 相似文献
103.
Dohyung Kim Hyeonsu Bang Hyoung Won Baac Jongmin Lee Phuoc Loc Truong Bum Ho Jeong Tamilselvan Appadurai Kyu Kwan Park Donghyeok Heo Vu Binh Nam Hocheon Yoo Kyeounghak Kim Daeho Lee Jong Hwan Ko Hui Joon Park 《Advanced functional materials》2023,33(14):2213064
Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca2+ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors. 相似文献
104.
Thanh Hai Nguyen Phan Khanh Linh Tran Van An Dinh Duy Thanh Tran Nam Hoon Kim Joong Hee Lee 《Advanced functional materials》2023,33(7):2210101
Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (FeMC)-carbide MXene (Mo2TiC2Tx) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of FeMC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the FeMC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from FeMC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm−2, respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm−2 and 1.86 V at 0.4 A cm−2 in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications. 相似文献
105.
The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy. 相似文献
106.
Jiong Zhao Honggi Nam Thuc Hue Ly Seok Joon Yun Sera Kim Suyeon Cho Heejun Yang Young Hee Lee 《Small (Weinheim an der Bergstrasse, Germany)》2017,13(1)
Defects in bulk crystals can be classified into vacancies, interstitials, grain boundaries, stacking faults, dislocations, and so forth. In particular, the vacancy in semiconductors is a primary defect that governs electrical transport. Concentration of vacancies depends mainly on the growth conditions. Individual vacancies instead of aggregated vacancies are usually energetically more favorable at room temperature because of the entropy contribution. This phenomenon is not guaranteed in van der Waals 2D materials due to the reduced dimensionality (reduced entropy). Here, it is reported that the 1D connected/aggregated vacancies are energetically stable at room temperature. Transmission electron microscopy observations demonstrate the preferential alignment direction of the vacancy chains varies in different 2D crystals: MoS2 and WS2 prefer direction, while MoTe2 prefers direction. This difference is mainly caused by the different strain effect near the chalcogen vacancies. Black phosphorous also exhibits directional double‐chain vacancies along 〈01〉 direction. Density functional theory calculations predict that the chain vacancies act as extended gap (conductive) states. The observation of the chain vacancies in 2D crystals directly explains the origin of n‐type behavior in MoTe2 devices in recent experiments and offers new opportunities for electronic structure engineering with various 2D materials. 相似文献
107.
Mesoporous Germanium Anode Materials for Lithium‐Ion Battery with Exceptional Cycling Stability in Wide Temperature Range
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Sinho Choi Yoon‐Gyo Cho Jieun Kim Nam‐Soon Choi Hyun‐Kon Song Guoxiu Wang Soojin Park 《Small (Weinheim an der Bergstrasse, Germany)》2017,13(13)
Porous structured materials have unique architectures and are promising for lithium‐ion batteries to enhance performances. In particular, mesoporous materials have many advantages including a high surface area and large void spaces which can increase reactivity and accessibility of lithium ions. This study reports a synthesis of newly developed mesoporous germanium (Ge) particles prepared by a zincothermic reduction at a mild temperature for high performance lithium‐ion batteries which can operate in a wide temperature range. The optimized Ge battery anodes with the mesoporous structure exhibit outstanding electrochemical properties in a wide temperature ranging from ?20 to 60 °C. Ge anodes exhibit a stable cycling retention at various temperatures (capacity retention of 99% after 100 cycles at 25 °C, 84% after 300 cycles at 60 °C, and 50% after 50 cycles at ?20 °C). Furthermore, full cells consisting of the mesoporous Ge anode and an LiFePO4 cathode show an excellent cyclability at ?20 and 25 °C. Mesoporous Ge materials synthesized by the zincothermic reduction can be potentially applied as high performance anode materials for practical lithium‐ion batteries. 相似文献
108.
Tran Huu Nam Ken Goto Kahori Oshima E.V.A. Premalal Yoshinobu Shimamura Yoku Inoue 《Advanced Composite Materials》2016,25(1):73-86
A solid-state drawing and winding process was done to create thin aligned carbon nanotube (CNT) sheets from CNT arrays. However, waviness and poor packing of CNTs in the sheets are two main weaknesses restricting their reinforcing efficiency in composites. This report proposes a simple press-drawing technique to reduce wavy CNTs and to enhance dense packing of CNTs in the sheets. Non-pressed and pressed CNT/epoxy composites were developed using prepreg processing with a vacuum-assisted system. Effects of pressing on the mechanical properties of the aligned CNT sheets and CNT/epoxy composites were examined. Pressing with distributed loads of 147, 221, and 294 N/m showed a substantial increase in the tensile strength and the elastic modulus of the aligned CNT sheets and their composites. The CNT sheets under a press load of 221 N/m exhibited the best mechanical properties found in this study. With a press load of 221 N/m, the pressed CNT sheet and its composite, respectively, enhanced the tensile strength by 139.1 and 141.9%, and the elastic modulus by 489 and 77.6% when compared with non-pressed ones. The pressed CNT/epoxy composites achieved high tensile strength (526.2 MPa) and elastic modulus (100.2 GPa). Results show that press-drawing is an important step to produce superior CNT sheets for development of high-performance CNT composites. 相似文献
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