Tendon-driven systems have become popular and efficient solutions for remotely positioning motors and actuation systems in various mechanisms. They address specific needs such as reducing the weight and inertia of mov...
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ISBN:
(纸本)9798331516246;9798331516239
Tendon-driven systems have become popular and efficient solutions for remotely positioning motors and actuation systems in various mechanisms. They address specific needs such as reducing the weight and inertia of moving components and minimizing their dimensions. Tendon-actuated systems offer benefits like low cost and the absence of backlash, leading to significant interest in tendon modeling within the scientific community. This interest spans from analytical solutions with inextensible tendons to computer-aided engineering (CAE) approaches utilizing tendons as deformable elements. However, developing tendon-based actuation systems through CAE tools has been limited due to substantial computational requirements and the challenge of obtaining reliable, technically applicable results. Finite element analysis (FEA) becomes complex and unsuitable for the design phase due to the significant deformation and displacement resulting from tendons' flexible behavior. Consequently, research into robotic systems actuated by tendons typically relies on analytical calculus and data from costly prototypes, requiring significant time and investment. Moreover, incorporating soft structures makes creating a comprehensive analytical model of the entire system in three-dimensional space daunting or even impossible, particularly with more complex soft structures, thus making FEA analysis the only viable approach. This work reviews the main solutions explored in the literature for solving these systems, aiming to provide designers with a broader view of the possible techniques that can be used based on the specific application.
This paper designs the overall logical architecture of the big data security supervision platform. Firstly, this paper proposes a random address generation mechanism compatible with IPv6 Internet transmission, a rando...
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The COVID-19 pandemic has transformed nursing education worldwide. Due to the globally applied restrictions of interpersonal interactions, many educational institutions transitioned from traditional to computer-aided ...
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The COVID-19 pandemic has transformed nursing education worldwide. Due to the globally applied restrictions of interpersonal interactions, many educational institutions transitioned from traditional to computer-aided nursing education pedagogies. However, an obligatory change, this digital transformation in nursing education, has been deemed promising by students and academics, yet raising concerns about the effectiveness of innovative nursing pedagogies. Hence, this systematic literature review aims to investigate the state of the art of computer-aided nursing pedagogies in the post-COVID-19 era and provide recommendations for further research investigation. Specifically, it utilises a mixed methods approach to examine (1) the evolution of computer-aided nursing pedagogies before and after COVID-19;(2) their effectiveness against traditional methods in terms of knowledge, skills acquisition and self-efficiency;and (3) nursing students' experiences and opinions when exposed to computer-aided nursing education pedagogies. For this purpose, several databases (PubMed, MEDLINE, CINAHL Complete, Academic Search Elite, ieee, ACM, Scopus, ERIC and Cochrane Library (controlled trial requests) were searched, initially retrieving 802 articles published between 2013-2023. After removing duplicates, exclusion criteria and assessment for eligibility, the number of articles assessed for eligibility was reduced to 78 conducted in 20 different countries. The articles comprised quantitative research (n=37), including Randomised control Trials (n=14) and Quasi-experimental studies (n=23), and qualitative research (n=41) including observational studies (n=14), mixed-methods methodological design (n=15), pilot studies (n=7) and conference papers (n=5). Moreover, this SLR utilised the Joanna Briggs Institute (JBI) methodological approach for conducting a mixed-methods systematic review (MMSR) and provided a narrative synthesis of all studies. The results of this mixed-methods SLR sugges
The signal plane layout of railway station is drawn according to the line diagram of the station yard, which can correctly reflect the layout of the main outdoor equipment and the setting location, the use of lines an...
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Homomorphic encryption enables computations on the ciphertext to preserve data privacy. However, its practical deployment has been hindered by the significant computational overhead compared to the plaintext computati...
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Visualization technologies like AR/MR have transformed traditional design collaboration, significantly impacting power dynamics and conflicting interests among stakeholders. However, there is currently no comprehensiv...
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Recent advances in the architecture design for photonic accelerators have demonstrated great promise to accelerate deep neural network (DNN) applications, and also allude to the essential collaboration of the electron...
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ISBN:
(纸本)9798350322255
Recent advances in the architecture design for photonic accelerators have demonstrated great promise to accelerate deep neural network (DNN) applications, and also allude to the essential collaboration of the electronic subsystems for efficient logic arithmetic and memory access. However, available tools to design and evaluate photonic accelerators usually neglect the cross-stack effects or low-level details in real-world scenarios, ranging from programming-stack inefficiency to electronic peripheral implementation complexity. This frustrating fact makes it difficult to holistically estimate the performance metrics of a practical photonic-electronic collaborative computing system. In addition, until now, no toolchain can provide programmable, hardwarereconfigurable, and end-to-end rapid verification for photonic accelerators. Here we present FIONA, a Full-stack Infrastructure for Optical Neural Accelerator, which comprises a photonic-electronic co-simulation framework for multi-level design space exploration (DSE), and a transferable hardware prototyping template for physical verification. Specifically, the co-simulation framework consists of a functional simulator at the instruction set architecture (ISA) level to agilely verify the programming software stack and a register-transfer level (RTL) cycle-accurate simulator to precisely profile the overall system. We also demonstrate LightRocket as a case study of the FIONA toolchain to show the full workflow of designing a Turing-complete photonic accelerator system that supports arbitrary DNN workloads and on-chip training. The toolchain is open-sourced and available at https://***/hkust-fiona/.
Oil extraction screw presses are widely used in agriculture and food industry. In most cases, they are not equipped with complicated controlsystems due to the necessity of reducing their price and providing sufficien...
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ISBN:
(纸本)9798350378634;9798350378627
Oil extraction screw presses are widely used in agriculture and food industry. In most cases, they are not equipped with complicated controlsystems due to the necessity of reducing their price and providing sufficient reliability. Therefore, such presses cannot reach the best efficiency (productivity, performance), while processing different seeds and kernels. The present paper is focused on development of the enhanced control allowing for monitoring the operational parameters of the screw press and adjusting them in accordance with user-defined technologically prescribed ones. The research methodology contains a thorough analysis of the press design and operational peculiarities, and the development of functional (block), circuit, and breadboard diagrams of the controlsystem. In order to verify the initially stated ideas of control strategies, the simulation models of the controlsystems are implemented in the TinkerCAD and SolidWorks software, and the experimental prototype of the screw press is correspondingly improved. The presented controlsystem and press regulation strategies can be effectively implemented by engineers and technologists while developing new and enhancing existing designs of screw presses.
Existing hardware emulators use either FPGA or Boolean processors, which suffer from long compile time and poor debuggability (FPGA-based), or low emulation performance (Boolean processor-based). This work presents Sp...
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ISBN:
(纸本)9798350322255
Existing hardware emulators use either FPGA or Boolean processors, which suffer from long compile time and poor debuggability (FPGA-based), or low emulation performance (Boolean processor-based). This work presents Sphinx, a hybrid Boolean processor-FPGA hardware emulation platform aiming to overcome these shortcomings. Sphinx hardware is a new hybrid architecture that integrates software programmable Boolean processors and FPGAs. Sphinx software is a compilation framework that conducts incremental design partitioning and implements the design-under-test components on Boolean processors and the rest on FPGAs. Together, Sphinx enables an incremental emulation flow and demonstrates high emulation performance, fast compile turnarounds, and good debuggability.
Analog Computation-in-Memory (CiM) with ReRAM accelerates the MAC operations of neural networks (NNs). A major issue of CiM is the area and power consumption of analog-to-digital converters (ADCs). This work proposes ...
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ISBN:
(纸本)9798350322255
Analog Computation-in-Memory (CiM) with ReRAM accelerates the MAC operations of neural networks (NNs). A major issue of CiM is the area and power consumption of analog-to-digital converters (ADCs). This work proposes a low-bit A/D conversion system to improve area/energy efficiency. However, the application-level accuracy is degraded due to quantization error and the limited range of low-bit ADC. To determine the optimal ADC range systematically while maintaining application-level accuracy, Layer Input/Output (I/O) Range Training (LIORAT) is proposed. LIORAT simultaneously trains the weights of a NN and the I/O range of each NN layer. Additionally, a digital ReRAM look-up table (LUT) is placed just after the ADC in the proposed A/D conversion system. Digital ReRAM LUT is used for non-MAC operations in the NN, such as batch normalization (BN). The values of the LUT are uniquely determined by the BN parameters and I/O ranges obtained by LIORAT. The application-level accuracy degradation caused by the ADC non-linearity and ReRAM weight errors can be compensated only by retraining BN parameters. Hence, the accuracy is recovered by updating digital LUT with the retrained BN parameters. Weight error compensation by updating digital LUT requires lower write accuracy compared to analog weight rewriting. ResNet-32 trained with the proposed LIORAT achieves 87.1% inference accuracy on the CIFAR-10 dataset with only 10% LUT area overhead, 4-bit weights, 2-bit DAC, and 4-bit ADC. By updating the LUT, the magnitude of the tolerable error is more than doubled compared to the case without compensation.
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