Spatial Analysis of Subway Ridership: Rainfall and Ridership

In-vehicle congestion of the urban railway system is the most important indicator to reflect the operation state of the urban railway. To provide the good service quality of urban railway, the crowdedness of the urban railway should be managed appropriately. The weather is one of the critical factors for the crowdedness. That is because even though the crowdedness of the urban railway is the same, passengers feel more uncomfortable in rainy weather condition. Indeed if specific sections and stations suddenly are concentrated excessive demand, it will lead far more serious problem. Therefore, this study analysis the relationship between the number of urban railway passenger and rainfall intensity in Seoul metropolitan subway system and then conducts the spatial analysis to deduct passenger demand patterns. This study is expected to be useful base study in order to manage the congestion at the urban railway station effectively by considering the different rainfall intensity

Radiation-induced thermal conductivity degradation modeling of zirconium

This study presents a radiation-induced thermal conductivity degradation (TCD) model of zirconium as compared to the conventional UO2 TCD model. We derived the governing factors of the radiation-induced TCD model, such as maximum TCD value and temperature range of TCD. The maximum TCD value was derived by two methods, in which 1) experimental result of 32 % TCD was directly utilized as the maximum TCD value and 2) a theoretical approach based on dislocation was applied to derive the maximum TCD value. Further, the temperature range of TCD was determined to be 437–837 K by 1) experimental results of post-annealing of irradiation hardening as compared to 2) the rate theory and thermal equilibrium. Consequently, the radiation-induced TCD model of zirconium was derived to be fr=1−0.321+exp⁡{(T−637)/45} . Because the thermal conductivity of zirconium is one of the factors determining the storage and transport system, this newly proposed model could improve the safety analysis of spent fuel storage systems

Energy-efficient XNOR-free In-Memory BNN Accelerator with Input Distribution Regularization

SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests as a platform for energy-efficient edge neural network computing thanks to their compactness in terms of hardware and neural network parameter size. However, previous works had to modify SRAM cells to support XNOR operations on memory array resulting in limited area and energy efficiencies. In this work, we present a conversion method which replaces the signed inputs (+1/-1) of BNN with the unsigned inputs (1/0) without computation error, and vice versa. The method enables BNN computing on conventional 6T SRAM arrays and improves area and energy efficiencies. We also demonstrate that further energy saving is possible by skewing the distribution of binary input data based on regularization during network training. Evaluation results show that the proposed techniques improve the inference energy efficiency by up to 9.4x for various benchmarks over previous works.1

An ANP-based technology network for identification of core technologies: A case of telecommunication technologies

There have often been attempts to examine technological structure and linkage as a network. Network analysis has been mainly employed with various centrality measures to identify core technologies in a technology network. None of the existing centrality measures, however, can successfully capture indirect relationships in a network. To address this limitation, this study proposes a novel approach based on the analytic network process (ANP) to identification of core technologies in a technology network. Since the ANP is capable of measuring the relative importance that captures all the indirect interactions in a network, the derived "limit centrality" indicates the importance of a technology in terms of impacts on other technologies, taking all the direct and indirect influences into account. The proposed approach is expected to allow technology planners to understand current technological trends and advances by identifying core technologies based on limit centralities. Using patent citation data as proxy for interactions between technologies, a case study on telecommunication technologies is presented to illustrate the proposed approach. (C) 2007 Elsevier Ltd. All rights reserved.Demirtas EA, 2009, COMPUT IND ENG, V56, P677, DOI 10.1016/j.cie.2006.12.006Gencer C, 2007, APPL MATH MODEL, V31, P2475, DOI 10.1016/j.apm.2006.10.002Yuksel I, 2007, INFORM SCIENCES, V177, P3364, DOI 10.1016/j.ins.2007.01.001Chang CW, 2007, INFORM SCIENCES, V177, P3383, DOI 10.1016/j.ins.2007.02.010Jharkharia S, 2007, OMEGA-INT J MANAGE S, V35, P274, DOI 10.1016/j.omega.2005.06.005Shin J, 2007, INF ECON POLICY, V19, P249, DOI 10.1016/j.infoecopol.2007.01.003Ayag Z, 2007, J ENG DESIGN, V18, P209, DOI 10.1080/09544820600752740Wu WW, 2007, EXPERT SYST APPL, V32, P841, DOI 10.1016/j.eswa.2006.01.029Bayazit O, 2007, INT J PROD ECON, V105, P79, DOI 10.1016/j.ijpe.2005.12.009KIM YG, 2007, EXPERT SYSTEMS APPLWEI WL, 2007, J ENG DESIGNLIN YH, 2007, EXPERT SYSTEMS APPLMulebeke JAW, 2006, J ENG TECHNOL MANAGE, V23, P337, DOI 10.1016/j.jengtecman.2006.08.004Agarwal A, 2006, EUR J OPER RES, V173, P211, DOI 10.1016/j.ejor.2004.12.005Kahraman C, 2006, EUR J OPER RES, V171, P390, DOI 10.1016/j.ejor.2004.09.016Gungor A, 2006, COMPUT IND ENG, V50, P35, DOI 10.1016/j.cie.2005.12.002HAN YJ, 2006, WORLD PATENT INFORM, V28, P235, DOI DOI 10.1016/J.WPI.2006.01.015*USPTO, 2006, OV US PAT CLASS SYSTChen DZ, 2005, SCIENTOMETRICS, V64, P31Chung SH, 2005, INT J PROD ECON, V96, P15, DOI 10.1016/j.ijpe.2004.02.006Cheng EWL, 2005, J CONSTR ENG M ASCE, V131, P459, DOI 10.1061/(ASCE)0733-9364(2005)131:4(459)Lai KK, 2005, INFORM PROCESS MANAG, V41, P313, DOI 10.1016/j.ipm.2003.11.004WARTBURG I, 2005, RES POLICY, V34, P1591Borgatti SP, 2005, SOC NETWORKS, V27, P55, DOI 10.1016/j.socnet.2004.11.008*OECD, 2005, OECD HDB EC GLOB INDNiemira MP, 2004, INT J FORECASTING, V20, P573, DOI 10.1016/j.ijforecast.2003.09.013Reitzig M, 2004, RES POLICY, V33, P939, DOI 10.1016/j.respol.2004.02.004YOON B, 2004, J HIGH TECHNOLOGY MA, V15, P37GANGULI P, 2004, WORLD PATENT INF, V26, P61, DOI 10.1016/j.wpi.2003.10.015Harhoff D, 2003, RES POLICY, V32, P1343ERNST H, 2003, WORLD PATENT INFORM, V25, P233, DOI 10.1016/S0172-2190(03)00077-2Karsak EE, 2003, COMPUT IND ENG, V44, P171Lacasa ID, 2003, SCIENTOMETRICS, V57, P175, DOI 10.1023/A:1024133517484SCHAPPER MA, 2003, PROPOSAL CORE LIST IBreitzman A, 2002, RES TECHNOL MANAGE, V45, P28MEADE L, 2002, IEEE T ENG MANAGE, V49, P22Bonacich P, 2001, SOC NETWORKS, V23, P191HIRSCHEY M, 2001, PACIFIC BASIN FINANC, V9, P65HALL BH, 2001, 8498 NAT BUR EC RESRuhnau B, 2000, SOC NETWORKS, V22, P357Lee JW, 2000, COMPUT OPER RES, V27, P367*EIRMA, 2000, 55 EIRMA WORK GROUPMeade LM, 1999, INT J PROD RES, V37, P241LANJOUW J, 1999, 7345 NAT BUR EC RESMowery DC, 1998, RES POLICY, V27, P507Meade L, 1998, TRANSPORT RES E-LOG, V34, P201Ham RM, 1998, CALIF MANAGE REV, V41, P137JAFFE A, 1998, 6509 NAT BUR EC RESBRESCHI S, 1998, KNOWLEDGE PROXIMITYTRAJTENBERG M, 1997, EC INNOVATION NEW TE, V5, P19Archibugi D, 1996, TECHNOVATION, V16, P451Grupp H, 1996, J EVOL ECON, V6, P175SAATY TL, 1996, DECISION MAKING DEPENARIN F, 1994, SCIENTOMETRICS, V30, P147WASSERMAN S, 1994, SOCIAL NETWORK ANALCOURTIAL JP, 1993, SCIENTOMETRICS, V26, P231GELSING L, 1992, NATL SYSTEMS INNOVAT, P116GRILICHES Z, 1990, J ECON LIT, V28, P1661TRAJTENBERG M, 1990, RAND J ECON, V21, P172BASBERG BL, 1987, RES POLICY, V16, P131NARIN F, 1987, RES POLICY, V16, P143BASBERG BL, 1984, WORLD PATENT INFORM, V6, P158MARSDEN PV, 1984, J MATH SOCIOL, V10, P271SAATY TL, 1980, ANAL HIERARCHY PROCEFREEMAN LC, 1979, SOC NETWORKS, V1, P215BONACICH P, 1972, J MATH SOCIOL, V2, P1131

TAIM: Ternary Activation In-Memory Computing Hardware with 6T SRAM Array

© 2022 ACM.Recently, various in-memory computing accelerators for low precision neural networks have been proposed. While in-memory Binary Neural Network (BNN) accelerators achieved significant energy efficiency, BNNs show severe accuracy degradation compared to their full precision counterpart models. To mitigate the problem, we propose TAIM, an in-memory computing hardware that can support ternary activation with negligible hardware overhead. In TAIM, a 6T SRAM cell can compute the multiplication between ternary activation and binary weight. Since the 6T SRAM cell consumes no energy when the input activation is 0, the proposed TAIM hardware can achieve even higher energy efficiency compared to BNN case by exploiting input 0's. We fabricated the proposed TAIM hardware in 28nm CMOS process and evaluated the energy efficiency on various image classification benchmarks. The experimental results show that the proposed TAIM hardware can achieve ∼ 3.61× higher energy efficiency on average compared to previous designs which support ternary activation.N

Mapping Binary ResNets on Computing-In-Memory Hardware with Low-bit ADCs

Implementing binary neural networks (BNNs) on computing-in-memory (CIM) hardware has several attractive features such as small memory requirement and minimal overhead in peripheral circuits such as analog-to-digital converters (ADCs). On the other hand, one of the downsides of using BNNs is that it degrades the classification accuracy. Recently, ResNet-style BNNs are gaining popularity with higher accuracy than conventional BNNs. The accuracy improvement comes from the high-resolution skip connection which binary ResNets use to compensate the information loss caused by binarization. However, the high-resolution skip connection forces the CIM hardware to use high-bit ADCs again so that area and energy overhead becomes larger. In this paper, we demonstrate that binary ResNets can be also mapped on CIM with low-bit ADCs via aggressive partial sum quantization and input-splitting combined with retraining. As a result, the key advantages of BNN CIM such as small area and energy consumption can be preserved with higher accuracy.1