Warranty Period and Product Price Optimization for Remanufactured Products

This study considers a remanufactured electrical product under a tiered warranty policy. Warranty is key in ensuring a good manufacturer—consumer relationship. Manufacturers hope to minimize warranty costs while consumers believe that good warranty promises better product quality and reliability. This Thesis presents an optimal warranty period from the perspective of a manufacturer to maximize the total expected profits, while ensuring sustained consumer relation. We use real data from a local company with a global supply chain to provide a numerical example

How Can China Learn from the Experience of the US to Improve the Export Control System of Aerospace Products

Aerospace products are different from ordinary commodities and usually have the characteristic of military use or dual-use application. This paper examines the US export policy system for aerospace products, including research on relevant US laws, policy institutions, policy instruments, and process mechanisms. By comparing the institutional differences between China and the United States, this paper analyzes the characteristics of the US export control system and the shortcomings of China's export control system, and then provides reference for the development of China's aerospace industry and the reform of China's export control policies. Keywords: export control; aerospace; international trade; policy research DOI: 10.7176/IAGS/72-03 Publication date:May 31st 201

A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene

An Improved Kernel and Parameterized Algorithm for Almost Induced Matching

An induced subgraph is called an induced matching if each vertex is a degree-1 vertex in the subgraph. The \textsc{Almost Induced Matching} problem asks whether we can delete at most $k$ vertices from the input graph such that the remaining graph is an induced matching. This paper studies parameterized algorithms for this problem by taking the size $k$ of the deletion set as the parameter. First, we prove a $6k$-vertex kernel for this problem, improving the previous result of $7k$. Second, we give an $O^*(1.6765^k)$-time and polynomial-space algorithm, improving the previous running-time bound of $O^*(1.7485^k)$.Comment: TAMC 202

Temporal Interaction -- Bridging Time and Experience in Human-Computer Interaction

Traditional static user interfaces (UI) have given way to dynamic systems that can intelligently adapt to and respond to users' changing needs. Temporal interaction is an emerging field in human-computer interaction (HCI), which refers to the study and design of UI that are capable of adapting and responding to the user's changing behavioral and emotional states. By comprehending and incorporating the temporal component of user interactions, it focuses on developing dynamic and individualized user experiences. This idea places a strong emphasis on the value of adjusting to user behavior and emotions in order to create a more unique and interesting user experience. The potential of temporal interaction to alter user interface design is highlighted by this paper's examination of its capacity to adjust to user behavior and react to emotional states. Designers can create interfaces that respond to the changing demands, emotions, and behaviors of users by utilizing temporal interactions. This produces interfaces that are not only highly functional but also form an emotional connection with the users.Comment: 8 page

Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI

Simultaneous EEG-fMRI acquisition and analysis technology has been widely used in various research fields of brain science. However, how to remove the ballistocardiogram (BCG) artifacts in this scenario remains a huge challenge. Because it is impossible to obtain clean and BCG-contaminated EEG signals at the same time, BCG artifact removal is a typical unpaired signal-to-signal problem. To solve this problem, this paper proposed a new GAN training model - Single Shot Reversible GAN (SSRGAN). The model is allowing bidirectional input to better combine the characteristics of the two types of signals, instead of using two independent models for bidirectional conversion as in the past. Furthermore, the model is decomposed into multiple independent convolutional blocks with specific functions. Through additional training of the blocks, the local representation ability of the model is improved, thereby improving the overall model performance. Experimental results show that, compared with existing methods, the method proposed in this paper can remove BCG artifacts more effectively and retain the useful EEG information.Comment: 8 pages, 5 figures, 1 tabl

Get Out of the Valley: Power-Efficient Address Mapping for GPUs

GPU memory systems adopt a multi-dimensional hardware structure to provide the bandwidth necessary to support 100s to 1000s of concurrent threads. On the software side, GPU-compute workloads also use multi-dimensional structures to organize the threads. We observe that these structures can combine unfavorably and create significant resource imbalance in the memory subsystem causing low performance and poor power-efficiency. The key issue is that it is highly application-dependent which memory address bits exhibit high variability. To solve this problem, we first provide an entropy analysis approach tailored for the highly concurrent memory request behavior in GPU-compute workloads. Our window-based entropy metric captures the information content of each address bit of the memory requests that are likely to co-exist in the memory system at runtime. Using this metric, we find that GPU-compute workloads exhibit entropy valleys distributed throughout the lower order address bits. This indicates that efficient GPU-address mapping schemes need to harvest entropy from broad address-bit ranges and concentrate the entropy into the bits used for channel and bank selection in the memory subsystem. This insight leads us to propose the Page Address Entropy (PAE) mapping scheme which concentrates the entropy of the row, channel and bank bits of the input address into the bank and channel bits of the output address. PAE maps straightforwardly to hardware and can be implemented with a tree of XOR-gates. PAE improves performance by 1.31 x and power-efficiency by 1.25 x compared to state-of-the-art permutation-based address mapping

Hydrogels enable negative pressure in water for efficient heat utilization and transfer

Metastable water in negative pressure can provide giant passive driving pressure up to several megapascals for efficient evaporation-driven flow, however, the practical applications with negative pressure are rare due to the challenges of generating and maintaining large negative pressure. In this work, we report a novel structure with thin hydrogel films as evaporation surfaces and robust porous substrates as the supports, and obtain a high negative pressure of -1.61 MPa through water evaporation. Molecular dynamics simulations elucidate the essential role of strong interaction between water molecules and polymer chains in generating the negative pressure. With such a large negative pressure, we demonstrate a streaming potential generator that spontaneously converts environmental energy into electricity and outputs a voltage of 1.06 V. Moreover, we propose a "negative pressure heat pipe" for the first time, which achieves a high heat transfer density of 11.2 kW cm-2 with a flow length of 1 m, showing the potential of negative pressure in efficient heat utilization and transfer.Comment: 43 pages, 18 figure