Comparison of the robustness-based optimal designs of water distribution systems in three different formulations

Jung, Donghwi,Kang, Doosun,Chung, Gunhui,Kim, Joong Hoon IWA Publishing 2013 Journal of hydroinformatics Vol.15 No.4

<P>Robustness is generally defined as a system's ability to stay within satisfactory bounds against variations in system factors. Recently, robustness has been indicated to be a useful objective function for the optimal design of water distribution systems (WDSs). While various formulations are possible to represent WDS robustness, few efforts have been made to compare the performances of these formulations. This study examined three potential formulations for quantifying system robustness to provide guidelines on the usage of a robustness index. Giustolisi <I>et al</I>.'s robustness index (see Giustolisi <I>et al.</I> (2009) ‘Deterministic versus stochastic design of water distribution networks’, <I>J. Water Resour. Plann. Manage.</I> <B>135</B> (2), 117–127) was adopted to calculate nodal robustness, while the system robustness was defined using three different formulations: (1) minimum among nodal robustness values; (2) total sum of nodal robustness; and (3) sum of nodal robustness at multiple critical nodes. The three proposed formulations were compared through application to identify the most appropriate one for enhancing system robustness in general; three representative benchmark networks were optimally designed to minimize the economic cost while maximizing the system robustness.</P>

Robustness Management with Data Quality in Information Systems

Zhiting Song,Yanming Sun 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.9

The data quality in information systems affects system robustness significantly. Although a large body of research focuses on the issue of data quality, seldom has literature been done to develop effective control policies to manage system robustness associated with data errors. A process-oriented methodology is proposed to manage system robustness measured by performance, control cost and control time, which is achieved through establishing formal model of information systems and mathematical optimization models of system robustness. The proposed methodology captures how structural and functional characteristics of tasks affect system robustness and finds the optimal control policies, which facilitates the robustness-based design and management of information systems respectively. The methodology is illustrated in the case study.

Robustness-Improvement Strategy and Modeling under Uncertainty of Chinese Catering Service Supply Chain

Juanqiong Gou,Jing Xiang,Shujun Zhang,Wei Dai 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.7

Catering Service Supply Chain (CSSC) is a new supply chain, of which Catering Service Integrator (CSI) is in the center accompany with Functional Catering Service Provider (FCSP). The core objective of CSSC is to provide different catering enterprises with supply chain services. Instability, non-standardization, specialty and diversity characteristics of the Chinese catering industry inevitably lead to demand uncertainty of CSSC, directly resulting in the uncertainty of operation and supply side. The uncertainty of supply chain system is the basis of robust optimization study, under the influence of uncertainty, robustness is extremely important to ensure stable income and continuous operation of supply chain. This research classifies various uncertainty scenarios, based on robust optimization method. Two strategies, service coordination with widely knowledge sharing, and synchronous decision with intelligent inducement, are designed to induce and integrate personalized demand to get the scale effect and the coordination of the entire supply chain. From a point of quantitative view, this research builds up robust optimization model to improve robustness of CSSC, deposing the target into two coordinated parts. The coordination is of great sense, including two objectives simultaneously, the first one aims to maximize the delivery rate of order demand, the second one aims to optimize profit of CSI and FCSP. According to data examination, the coordinative strategies and robust optimization model can effectively control the uncertainty of the whole supply chain, and then improve the robustness of CSSC.

악성코드 변종 분석을 위한 AI 모델의 Robust 수준 측정 및 개선 연구

이은규,정시온,이현우,이태진 한국정보보호학회 2022 정보보호학회논문지 Vol.32 No.5

Today, AI(Artificial Intelligence) technology is being extensively researched in various fields, including the field of malware detection. To introduce AI systems into roles that protect important decisions and resources, it must be a reliable AI model. AI model that dependent on training dataset should be verified to be robust against new attacks. Rather than generating new malware detection, attackers find malware detection that succeed in attacking by mass-producing strains of previously detected malware detection. Most of the attacks, such as adversarial attacks, that lead to misclassification of AI models, are made by slightly modifying past attacks. Robust models that can be defended against these variants is needed, and the Robustness level of the model cannot be evaluated with accuracy and recall, which are widely used as AI evaluation indicators. In this paper, we experiment a framework to evaluate robustness level by generating an adversarial sample based on one of the adversarial attacks, C&W attack, and to improve robustness level through adversarial training. Through experiments based on malware dataset in this study, the limitations and possibilities of the proposed method in the field of malware detection were confirmed. 오늘날 AI(Artificial Intelligence) 기술은 악성코드 분야를 비롯하여 다양한 분야에서 광범위하게 연구되고 있다. 중요한 의사결정 및 자원을 보호하는 역할에 AI 시스템을 도입하기 위해서는 신뢰할 수 있는 AI 모델이어야 한다. 학습 데이터셋에 의존적인 AI 모델은 새로운 공격에 대해서도 견고한지 확인이 필요하다. 공격자는 악성코드를 새로 생성하기보단, 기존에 탐지되었던 악성코드의 변종을 대량 생산하여 공격에 성공하는 악성코드를 탐색다. AI 모델의 Misclassification을 유도하는 Adversarial attack과 같이 대부분의 공격은 기존 공격에 약간에 변형을 가해 만든 공격들이다. 이러한 변종에도 대응 가능한 Robust한 모델이 필요하며, AI 평가지표로 많이 사용되는 Accuracy, Recall 등으로는 모델의 Robustness 수준을 측정할 수 없다. 본 논문에서는 Adversarial attack 중 하나인 C&W attack을 기반으로 Adversarial sample을 생성하여 Robustness 수준을 측정하고 Adversarial training 을 통해 Robustness 수준을 개선하는 방법을 실험한다. 본 연구의 악성코드 데이터셋 기반 실험을 통해 악성코드 분야에서 해당 제안 방법의 한계 및 가능성을 확인하였다.

A Quantitative Method for Seismic Robustness of RC Frame Considering Resistance Vulnerability of Column and Storey Drift Ratios

Guohua Sheng,Shengji Jin,Lintao Ma,Quan Bai,Chao Xu,Xiaoyu Wang 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.1

The concept of seismic robustness is proposed by combining the concept of seismic performance and structural robustness. The existing qualitative, quantitative and evaluation methods of seismic robustness are all direct researched on the whole structure, and the influence mechanism of its internal components on the overall seismic robustness is still unclear. It is very important to establish a clear relationship between component design and structural seismic robustness for the structural design, reinforcement design and final evaluation of structural seismic robustness. Based on this, taking the column as the starting point, a quantitative method for the seismic robustness of RC frame by the seismic robustness index is proposed, which takes into account the resistance vulnerability of column and influence of column on the storey drift ratios (SDRs). In which, the resistance vulnerability is represented by the defined control vulnerability coefficient Pimax, and the influence on the SDRs are represented by the storey drift ratio importance coefficients (SDRCs) . The method not only reflects the essential mechanical properties of the column, but also reflects the effects brought about by different SALs. The feasibility of the method is demonstrated by numerical examples of two types of failures (assuming single column and two columns failure), and four optimization design proposals are proposed for it. The analysis shows that  of the target columns to the floor where the target columns located are obviously greater than those on the remaining floors of the target frame. The seismic robustness index R decreases sharply with the increase of the seismic action level (SAL). R is different compare single column failure with two columns failure under 4 SALs. The most effective way to improve the R of the frame under a certain SAL is to retrofit its control column.

A Domain-independent Dual-image based Robust Reversible Watermarking

Xuejing Guo,Yixiang Fang,Junxiang Wang,Wenchao Zeng,Yi Zhao,Tianzhu Zhang,Shi Yun-Qing 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

Development of Robust-SDP for improving dam operation to cope with non-stationarity of climate change

Yoon Hae Na,Seo Seung Beom,Kim Young-Oh 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.11

기존의 저수지 운영 연구들은 미래의 기후가 과거와 유사하다는 정상성의 가정을 전제로 하였다. 하지만 기후의 비정상성으로 인해 불확실성이 더욱 커질 경우에는 큰 불확실성에서도 안정된 최적해를 찾을 수 있는 로버스트 최적화 과정(Robust Optimization, 이하 RO)이 필요하다고 알려져 있다. RO는 입력자료의 비정상성으로 인해 야기되는 불확실성을 제어하는 로버스트 항을 목적함수에 추가하여 기존의 최적화 방법을 개선한다. 본 연구는 기후변화의 비정상성을 대비하는 저수지 운영규칙 산정을 위해 추계학적동적계획법(Stochastic Dynamic Programing, 이하 SDP)과 RO를 결합하는 Robust-SDP를 제안하였고, 이를 최근 4년간 가뭄을 겪었던 보령댐에 적용하였다. 즉, 비정상성이 반영된 미래 유입량 자료를 생성하고 이를 6가지의 평가지표와 2가지의 의사결정 지원그림을 사용하여 과거 유입량 자료로부터 산출된 저수지 운영규칙의 수행능력을 평가하 였다. 그 결과, Robust-SDP가 기후의 비정상성 하에서 극단적인 물 부족 사건의 발생률과 물 부족 사건의 실패의 크기를 감소시켰지만, 작은 크기의 물 부족 발생률은 증가하는 상충관계(trade-off)를 가져옴을 확인할 수 있었다. 이를 바탕으로 의사결정자가 우선시하는 평가지표의 결과에 따라 최적화 모형을 선택할 수 있음을 제안하였다. Previous studies on reservoir operation have been assumed that the climate in the future would be similar to that in the past. However, in the presence of climate non-stationarity, Robust Optimization (RO) which finds the feasible solutions under broader uncertainty is necessary. RO improves the existing optimization method by adding a robust term to the objective function that controls the uncertainty inherent due to input data instability. This study proposed Robust-SDP that combines Stochastic Dynamic Programming (SDP) and RO to estimate dam operation rules while coping with climate non-stationarity. The future inflow series that reflect climate non-stationarity were synthetically generated. We then evaluated the capacity of the dam operation rules obtained from the past inflow series based on six evaluation indicators and two decision support schemes. Although Robust-SDP was successful in reducing the incidence of extreme water scarcity events under climate non-stationarity, there was a trade-off between the number of extreme water scarcity events and the water scarcity ratio. Thus, it is proposed that decision-makers choose their optimal rules in reference to the evaluation results and decision support illustrations.

기후변화의 비정상성 대비 댐 운영 개선을 위한 Robust-SDP의 개발

윤해나,서승범,김영오 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.S-1

Previous studies on reservoir operation have been assumed that the climate in the future would be similar to that in the past. However, in the presence of climate non-stationarity, Robust Optimization (RO) which finds the feasible solutions under broader uncertainty is necessary. RO improves the existing optimization method by adding a robust term to the objective function that controls the uncertainty inherent due to input data instability. This study proposed Robust-SDP that combines Stochastic Dynamic Programming (SDP) and RO to estimate dam operation rules while coping with climate non-stationarity. The future inflow series that reflect climate non-stationarity were synthetically generated. We then evaluated the capacity of the dam operation rules obtained from the past inflow series based on six evaluation indicators and two decision support schemes. Although Robust-SDP was successful in reducing the incidence of extreme water scarcity events under climate non-stationarity, there was a trade-off between the number of extreme water scarcity events and the water scarcity ratio. Thus, it is proposed that decision-makers choose their optimal rules in reference to the evaluation results and decision support illustrations. 기존의 저수지 운영 연구들은 미래의 기후가 과거와 유사하다는 정상성의 가정을 전제로 하였다. 하지만 기후의 비정상성으로 인해 불확실성이 더욱 커질 경우에는 큰 불확실성에서도 안정된 최적해를 찾을 수 있는 로버스트 최적화 과정(Robust Optimization, 이하 RO)이 필요하다고 알려져있다. RO는 입력자료의 비정상성으로 인해 야기되는 불확실성을 제어하는 로버스트 항을 목적함수에 추가하여 기존의 최적화 방법을 개선한다. 본 연구는 기후변화의 비정상성을 대비하는 저수지 운영규칙 산정을 위해 추계학적동적계획법(Stochastic Dynamic Programing, 이하 SDP)과 RO를 결합하는 Robust-SDP를 제안하였고, 이를 최근 4년간 가뭄을 겪었던 보령댐에 적용하였다. 즉, 비정상성이 반영된 미래 유입량 자료를 생성하고 이를 6가지의 평가지표와 2가지의 의사결정 지원그림을 사용하여 과거 유입량 자료로부터 산출된 저수지 운영규칙의 수행능력을 평가하였다. 그 결과, Robust-SDP가 기후의 비정상성 하에서 극단적인 물 부족 사건의 발생률과 물 부족 사건의 실패의 크기를 감소시켰지만, 작은 크기의 물 부족 발생률은 증가하는 상충관계(trade-off)를 가져옴을 확인할 수 있었다. 이를 바탕으로 의사결정자가 우선시하는 평가지표의 결과에 따라 최적화 모형을 선택할 수 있음을 제안하였다.

공급사슬에서의 리스크 대응 활동과 강건성 및 공급사슬성과 간의 관계에 대한 연구

박찬권(Park, Chan-Kwon),박성민(Park, Sung-Min) 한국물류학회 2021 물류학회지 Vol.31 No.3

본 연구는 공급사슬 네트워크에서 발생할 수 있는 리스크 요인들을 구분하고 이들 리스크 요인에 대하여 적절하게 대응하는활동이 리스크 발생 상황에서 공급사슬 회복탄력성의 일환인 강건성 역량으로 연결될 수 있으며, 이러한 강건성 역량이 궁극적으로 공급사슬성과로 나타날 수 있는가에 대하여 확인하는 것이다. 이러한 연구 목표를 달성하기 위하여 우리나라 제조기업체들 중108개 업체의 설문지를 연구에 활용하였으며, 2차에 걸쳐서 연구항목별로 신뢰성과 타당성을 확인하고 경로분석 방식으로 연구를진행하였다. 연구가설 검정 결과를 살펴보면 먼저 운송리스크 대응, 재고리스크 대응, 정보리스크 대응, 공급자리스크 대응은 강건성에 유의한 정(+)의 영향을 미친다. 그러나 시장리스크 대응은 강건성에 부(-)의 영향을 미치지만 유의하지는 않았으며, 예측리스크 대응은 강건성에 정(+)의 영향을 미치지만 유의하지는 않았다. 마지막으로 강건성은 공급사슬성과에 유의한 정(+)의 영향을 미치는 것으로 검정되었다. 또한 공급사슬 리스크 대응과 공급사슬성과 사이에서 강건성의 매개효과를 분석한 결과 강건성은 매개변수의 역할을 하는 것으로 검정 되었다. 따라서 리스크 대응 활동과 공급사슬 회복탄력성으로써 강건성, 공급사슬성과 간의 전체적인 관계구조에 대하여 실증하였다. This study categorizes risk factors that may occur in supply chain networks, and activities that respond appropriately to these risk factors can be linked to robustness capabilities as a part of supply chain resilience in the event of a risk. As a result, it is to check whether it can appear as a performance in the supply chain. In order to achieve this research goal, questionnaires of 108 manufacturers among Korean manufacturing companies were used for the study, and the reliability and validity of each study item were checked in the second phase, and the study was conducted in a path analysis method. Looking at the research hypothesis test results, first, transport risk response, inventory risk response, information risk response, and supplier risk response have a significant positive (+) effect on robustness. However, over-market risk response had a negative (-) effect on robustness, but not significant, while predictive risk response had a positive (+) effect on robustness, but not significant. Finally, robustness was tested to have a significant positive (+) effect on supply chain performance. Also, as a result of analyzing the mediating effect of robustness between supply chain risk response and supply chain performance, it was verified that robustness plays a role as a parameter. Therefore, the overall relationship structure between robustness and supply chain performance as risk response activities and supply chain resilience was demonstrated.

Robust Nonparametric Regression Method using Rank Transformation

Park, Dongryeon The Korean Statistical Society 2000 Communications for statistical applications and me Vol.7 No.2

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.