Simulation of the Long-Term Effects of Decentralized and Adaptive Investments in Cross-Agency Interoperable and Standard IT Systems

Governments have come under increasing pressure to promote horizontal flows of information across agencies, but investment in cross-agency interoperable and standard systems have been minimally made since it seems to require government agencies to give up the autonomies in managing own systems and its outcomes may be subject to many external and interaction risks. By producing an agent-based model using 'Blanche' software, this study provides policy-makers with a simulation-based demonstration illustrating how government agencies can autonomously and interactively build, standardize, and operate interoperable IT systems in a decentralized environment. This simulation designs an illustrative body of 20 federal agencies and their missions. A multiplicative production function is adopted to model the interdependent effects of heterogeneous systems on joint mission capabilities, and six social network drivers (similarity, reciprocity, centrality, mission priority, interdependencies, and transitivity) are assumed to jointly determine inter-agency system utilization. This exercise simulates five policy alternatives derived from joint implementation of three policy levers (IT investment portfolio, standardization, and inter-agency operation). The simulation results show that modest investments in standard systems improve interoperability remarkably, but that a wide range of untargeted interoperability with lagging operational capabilities improves mission capability less remarkably. Nonetheless, exploratory modeling against the varying parameters for technology, interdependency, and social capital demonstrates that the wide range of untargeted interoperability responds better to uncertain future states and hence reduces the variances of joint mission capabilities. In sum, decentralized and adaptive investments in interoperable and standard systems can enhance joint mission capabilities substantially and robustly without requiring radical changes toward centralized IT management.Public IT Investment, Interoperability, Standardization, Social Network, Agent-Based Modeling, Exploratory Modeling

EFFECT OF CARBON NANOFIBERS ON MICROSTRUCTURE AND PROPERTIES OF POLYMER NANOCOMPOSITES

Nano-modifiers are typically three orders of magnitude smaller in size than their micro-counterparts. At this scale, interactions between matrix molecules and the nano-modifiers can lead to novel physical and chemical properties of the resulting nanocomposites. Carbon nanofibers (CNFs) are a class of nano-modifiers that has received significant attention recently because they have superior electrical conductivity and mechanical properties with a high aspect ratio (length over diameter). Several recent research studies have focused on enhancing electrical and mechanical performance of composites in the presence of CNFs. However, the influence of CNFs on the structure of the polymer matrix is important in understanding the role CNFs have on the properties of nanocomposites, but this has not been thoroughly examined. Therefore, crystalline and orientational structure of CNF/polymer composites was investigated in this study. First, the microstructure of two different grades of CNFs, MJ (experimental) and PR (commercial), was investigated as a function of different thermal treatments. Using Raman spectroscopy and XRD analysis, an enhancement of crystallite size was observed after heat treatment at 2200°C. The crystallite thickness increased from 1.6±0.1 nm to 10.9±0.5 nm for MJ fibers and from 3.1±0.3 nm to 11.7±0.4 nm for PR fibers. Also, an increase in thermal oxidation stability for heat-treated CNFs was observed. BET adsorption isotherms showed a significant reduction of specific surface area of MJ fibers after the heat treatment, resulting from a decrease of pore volume. However, even after heat treatment, MJ fibers possessed a rougher surface than did PR fibers. The role of such nano-texture was studied on two distinct types of polymeric matrices: flexible-chain and semi rigid-rod polymers. Linear low density polyethylene (LLDPE), a flexible-chain polymer, is widely used for packaging applications because of its film-forming properties and good barrier characteristics. However, LLDPE has a poor electrical conductivity, which results in poor EMI/ESD shielding. Therefore, CNFs were incorporated into LLDPE to improve electrical conductivity. The Electrical percolation threshold was observed at approximately 15 wt% MJ (MJ15) and 30 wt% PR (PR30). Tensile modulus increased from 110 MPa for pure LLDPE to 200 MPa and 300 MPa for MJ15 and PR15, respectively. However, the tensile strength remained fairly unchanged at about 20 MPa. Strain-to-failure decreased from 690% for pure LLDPE to 460% and 120% for MJ15 and PR15, respectively. This indicates that the interfacial bonding of LLDPE matrix with MJ fibers is better than that with PR fibers, possibly due to rougher surface of the MJ fibers. Crystallization behavior of LLDPE nanocomposites was investigated in the presence of three types of CNFs (MJ, PR, and PRCVD). During non-isothermal crystallization studies, all three crystalline melting peaks for LLDPE matrix were observed in the presence of PRCVD fibers up to 15 wt% content. However, at only 1 wt% MJ fibers, the disappearance of the intermediate melting peak was observed. The broad melting peak at the lower temperature became larger, suggesting an increase in the relative content of thinner lamellae in the presence of MJ fibers. The larger and the rougher surface of MJ fibers observed from the nano-textural study contributes toward the different crystallization behavior of MJ/LLDPE composites. TEM micrographs of nanocomposites revealed transcrystallinity of LLDPE on the surface of CNFs. Further, a broader distribution of LLDPE lamellar thickness was observed in TEM images of MJ composites. The third major component of this research project was a study on the role of CNFs on a thermotropic liquid crystalline polymer (TLCP) matrix possessing a semi rigid-rod molecular structure. A variation of anisotropy of the TLCP was investigated in the presence of CNFs. Electrical percolation threshold was observed at approximately 5 wt% MJ fibers. Decrease of tensile modulus and strength was observed for composites. For a given type of flow, wide angle X-ray diffraction studies showed a decrease in Herman\u27s orientation parameter from 0.85 for pure V400P to 0.71 for 5 wt% MJ composites. Thus, the presence of CNFs led to a reduction of the overall anisotropy of the nematic phase in the nanocomposite. The disruption of molecular orientation of TLCPs was inferred by SEM and TEM analysis. SEM micrographs revealed a fibrillar structure for pure TLCPs at a macro-scale. However, this structure was not observed in composites at the same scale although micro-size fibrils were found with the addition of PR fibers. TEM micrographs displayed banded structures of pure TLCPs, but these structures were not significant in the vicinity of PR fibers. These results indicate that CNFs can help to reduce the severe anisotropy that is otherwise observed for TLCPs. In summary, this study establishes the significant role of CNFs as nano-modifiers that help modify the texture of the matrix while serving to enhance specific properties, such as electrical conductivity, for the nanocomposites

A correlation between the optical and mechanical properties of novel polysilane/polysiloxane nanocomposites

Polysilanes are ¥ò-conjugated conductive polymers with a one-dimensional Si backbone and organic substituted side chains. The conventional Wurtz-type coupling reaction has been used for preparing various alkyl substituted polysilanes, i.e. poly(di-n-hexyl)silane and poly(hexylmethyl)silane. For each polysilane examples with methoxy (-OCH3) and allyl (-CH2CH=CH2) end caps have been prepared. The solution UV absorption characteristics of the polysilanes at room temperature have been measured, from which the wavelength maximum of the UV-VIS absorption and the absorptivity have been obtained. Allyl end capped polysilanes have the same wavelength maximum as methoxy end capped polysilanes. However, the absorptivities of allyl end capped polysilanes are always higher than those of the methoxy end capped polysilanes, presumably as a result of the fact that they contain fewer defects. The conformation of poly(di-n-hexyl)silanes with methoxy end caps and polysilanes with allyl end caps in polymer networks has been studied. The polymer networks have been obtained by cross-linking vinyl terminated poly(dimethyl)siloxanes and methylhydrosiloxane-dimethylsiloxane copolymers. The correlation between the mechanical properties of the composites and the conformation of the polysilanes in the composites has been studied using rheology and UV-VIS spectroscopy. It has been determined that the conformation of poly(di-n-hexyl)silane is dependent on the flexibility of poly(di-n-hexyl)silane/polysiloxane composites. In addition, the thermochromism of the polysilane/polysiloxane composites has been studied. Noticeable differences in the transition behavior have been observed in comparison to polysilanes in solution and the solid state. The reduced degree of conformational freedom of the polysilanes results in different transition temperatures and a shoulder at longer wavelength at high temperature

Global HRTF Interpolation via Learned Affine Transformation of Hyper-conditioned Features

Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points is essential in immersive binaural audio rendering. Computing each individual's HRTFs is challenging, as traditional approaches require expensive time and computational resources, while modern data-driven approaches are data-hungry. Especially for the data-driven approaches, existing HRTF datasets differ in spatial sampling distributions of source positions, posing a major problem when generalizing the method across multiple datasets. To alleviate this, we propose a deep learning method based on a novel conditioning architecture. The proposed method can predict an HRTF of any position by interpolating the HRTFs of known distributions. Experimental results show that the proposed architecture improves the model's generalizability across datasets with various coordinate systems. Additional demonstrations using coarsened HRTFs demonstrate that the model robustly reconstructs the target HRTFs from the coarsened data.Comment: Submitted to Interspeech 202

B+-tree Index Optimization by Exploiting Internal Parallelism of Flash-based Solid State Drives

Previous research addressed the potential problems of the hard-disk oriented design of DBMSs of flashSSDs. In this paper, we focus on exploiting potential benefits of flashSSDs. First, we examine the internal parallelism issues of flashSSDs by conducting benchmarks to various flashSSDs. Then, we suggest algorithm-design principles in order to best benefit from the internal parallelism. We present a new I/O request concept, called psync I/O that can exploit the internal parallelism of flashSSDs in a single process. Based on these ideas, we introduce B+-tree optimization methods in order to utilize internal parallelism. By integrating the results of these methods, we present a B+-tree variant, PIO B-tree. We confirmed that each optimization method substantially enhances the index performance. Consequently, PIO B-tree enhanced B+-tree's insert performance by a factor of up to 16.3, while improving point-search performance by a factor of 1.2. The range search of PIO B-tree was up to 5 times faster than that of the B+-tree. Moreover, PIO B-tree outperformed other flash-aware indexes in various synthetic workloads. We also confirmed that PIO B-tree outperforms B+-tree in index traces collected inside the Postgresql DBMS with TPC-C benchmark.Comment: VLDB201

Differentiable Artificial Reverberation

Artificial reverberation (AR) models play a central role in various audio applications. Therefore, estimating the AR model parameters (ARPs) of a target reverberation is a crucial task. Although a few recent deep-learning-based approaches have shown promising performance, their non-end-to-end training scheme prevents them from fully exploiting the potential of deep neural networks. This motivates to introduce differentiable artificial reverberation (DAR) models which allows loss gradients to be back-propagated end-to-end. However, implementing the AR models with their difference equations "as is" in the deep-learning framework severely bottlenecks the training speed when executed with a parallel processor like GPU due to their infinite impulse response (IIR) components. We tackle this problem by replacing the IIR filters with finite impulse response (FIR) approximations with the frequency-sampling method (FSM). Using the FSM, we implement three DAR models -- differentiable Filtered Velvet Noise (FVN), Advanced Filtered Velvet Noise (AFVN), and Feedback Delay Network (FDN). For each AR model, we train its ARP estimation networks for analysis-synthesis (RIR-to-ARP) and blind estimation (reverberant-speech-to-ARP) task in an end-to-end manner with its DAR model counterpart. Experiment results show that the proposed method achieves consistent performance improvement over the non-end-to-end approaches in both objective metrics and subjective listening test results.Comment: Manuscript submitted to TASL

FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks

In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs); one is a speech-to-character RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N-best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.Comment: Accepted to SiPS 201

SME supply chain collaboration innovation using an online hub

노트 : Proceedings of the 8th International Conference on Innovation & Managemen