• 한국지능시스템학회논문지
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• 제1권2호
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• pp.16-34
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• 1991

It has been widely accepted that expert systems must reason from multiple sources of information that is to some degree evidential - uncertain, imprecise, and occasionally inaccurate - called evidential information. Evidence theory (Dempster/Shafet theory) provides one of the most general framework for representing evidential information compared to its alternatives such as Bayesian theory or fuzzy set theory. Many expert system applications require evidence to be specified in the continuous domain - such as time, distance, or sensor measurements. However, the existing evidence theory does not provide an effective approach for dealing with evidence about continuous variables. As an extension to Strat's pioneeiring work, this paper provides a new combination rule, a new method for mass function transffrmation, and a new method for rendering joint mass fuctions which are of great utility in evidence theory in the continuous domain.

• 대한원격탐사학회지
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• 제21권2호
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• pp.145-162
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• 2005

SAR 자료의 분류에서 토지 피복 구분 분류 정확도의 향상을 위해 이 논문은 다중 시기 SAR 자료를 이용한 분류에서의 특징 추출과 정보 융합 방법론을 제시하였다. 다중 시기 SAR 센서의 산란 특성을 고려하여 평균 후방 산란계수, 시간적 변이도와 긴밀도를 특징으로서 추출하였다. 이렇게 추출된 특징의 효율적인 응합을 위해 Dempster-Shafer theory of evidence(D-S 이론)와 퍼지 논리를 적용하였다. 특히 D-S 이론의 적용시 특징 기반 mass function 할당을 제안하였고, 퍼지 논리의 적용시 다양한 퍼지 결합 연산자의 결과를 비교하였다. 다중 시기 Radarsat-1 자료에의 적용 결과, 추출된 특징들은 서로 상호 보완적인 정보를 제공할 수 있으며 수계, 논과 도심지를 효율적으로 구분할 수 있었다. 그러나 산림과 밭은 구분이 애매한 경우가 나타났다. 정보 융합 방법론 측면에서, D-S 이론과 퍼지 Max와 Algebraic Sum 연산자를 제외한 다른 퍼지 연산자는 서로 유사한 분류 정확도를 나타내었다.

• Journal of Information Processing Systems
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• 제12권2호
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• pp.295-309
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• 2016

Spectrum sensing is an essential function that enables cognitive radio technology to explore spectral holes and resourcefully access them without any harmful interference to the licenses user. Spectrum sensing done by a single node is highly affected by fading and shadowing. Thus, to overcome this, cooperative spectrum sensing was introduced. Currently, the advancements in multiple antennas have given a new dimension to cognitive radio research. In this paper, we propose a multiple energy detector for cooperative spectrum sensing schemes based on the evidence theory. Also, we propose a reporting mechanism for multiple energy detectors. With our proposed system, we show that a multiple energy detector using a cooperative spectrum sensing scheme based on evidence theory increases the reliability of the system, which ultimately increases the spectrum sensing and reduces the reporting time. Also in simulation results, we show the probability of error for the proposed system. Our simulation results show that our proposed system outperforms the conventional energy detector system.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1243-1257
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• 2019

As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.

• 한국산업보건학회지
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• 제31권4호
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• pp.396-416
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• 2021

Objectives: This study was conducted to analyze the trends of government R&D (R&D) projects related to laboratory safety over the past 20 years. Methods: We collected publications from various databases(DBs) with words such as laboratory(ies), lab(s), researcher(s), laboratory worker(s), safety, environment, hazard(s), risk(s), and so on. Selected publications were analyzed by the research funds and the number of projects according to the investment subject and research characteristics. Results: About 93% of the total R&D budget went to government policy projects, not scientific research. Second, from the perspective of 'safety management activities', most of the research is related to management and inspection at the organizational level. Issues that need to be discussed at the national level like policy governance are not included. Third, focusing on the 'safety management cycle', there were few studies related to 'prediction' or 'post-response'. Fourth, when an analysis framework combining the perspectives of 'safety management activities' and 'safety management cycle' is applied, most of the budget is spent on infrastructure such as digital management systems, whereas basic knowledge for prevention and production of evidence was very few. Conclusions: In order to prevent policy planning without policy evaluation, implementation without strategy, and evaluation without evidence, it is necessary to expand investment in empirical research on risks, research on the effectiveness of current application methods, and research on theory development. The government budget for laboratory safety-related projects should be managed separately from the R&D budget for scientific research. Although less than 5% of the budget allocated to scientific research is the total budget, an optical illusion occurs because both the project budget and the scientific research budget are counted as R&D budgets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1489-1502
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• 2019

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.

• Journal of East Asia Management
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• 제1권1호
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• pp.1-27
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• 2020

Military experience has a great impact on a soldier ability to handle risks. Therefore, when those soldiers become managers, they may behave differently in making risky corporate decisions, especially in activities like the R&D investment. However, studies on how military experience affect R&D have been largely missing in the largest emerging economy, i.e. China, despite that the country hires a higher percentage of military managers than the US. In addition, it remains a question whether military managers affect the state-owned enterprises (SOEs) in China, as many of the corporate decisions are made by the government. This paper tries to address these questions. The imprinting theory and the upper echelon theory suggest that managers' personal experience can affect their behaviour, which in turn influences their corporate decisions. In this paper, we examine whether managers with military experience lead to higher R&D investment and whether such an effect exists in state-owned enterprises. Based on a sample of listed firms in China's A-share market over 2008-2017, we make two findings. First, companies with military managers have high R&D investment. By dividing managers' military positions into high and low rank, we find that companies tend to have higher (lower) R&D investment if their managers hold a high-rank (low-rank) position. Second, the effect of high-rank military managers on R&D investment is more pronounced if the manager is also the founder and the company is a non-state-owned enterprise. For low-ranking military managers, a stronger effect on R&D investment is also observed if they are also the founder, but whether their companies are state-owned or not has no impact on R&D investment. This study identifies managers' military experience as a contributing factors to corporate R&D investment in the largest emerging economy. This paper tests an implication of the imprinting theory and the upper echelon theory, i.e., managers' personal experience can affect their behaviour, which in turn influences their corporate decisions. Specifically, we focus on one aspect of personal experience - military experience - and look at whether it is beneficial to firms' technological innovation, therefore enriches the literature of managerial heterogeneity. Our findings on the influence of managers' military experience on firms' technological innovation can help us better understand the role of managers play in corporate decision making, and how managers' individual traits interact with the firm's characteristics.

• 가정과삶의질연구
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• 제22권6호
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• pp.191-199
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• 2004

The main purpose of this study was to examine the impact of religious participation on the depression of elder adults in USA. Specifically, this study examined how the influence of religious participation varied according to continuity or discontinuity of participation. Data from N=1,658 adults aged 65-90 who were respondents to two waves of the U.S. National survey of Families and Households 1987-1993 were used for these analyses. Depression was measured with a 12-item (of the original 20) modified version of the CES-D (Center for Epidemiological Studies-Depression). Multivariate regression models controlling for several demographic variables were estimated. Some clear evidence was found supporting activity theory and continuity theory That is, participating in a religious organization role at Time 2 but not Time 1 (T1 No - T2 Yes) and being continuously involved in religious organizations both at Time 1 and Time 2 (T1 Yes -T2 Yes) were associated with reduced depression, compared to continuous nonparticipation in religious organizations (71 No -72 No).

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.136-139
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• 2001

This paper develops a sensor based navigation method that utilizes fuzzy logic and the Dempster-Shafer evidence theory for mobile robot in uncertain environment. The proposed navigator consists of two behaviors: obstacle avoidance and goal seeking. To navigate reliably in the environment, we make a map building process before the robot finds a goal position and create a robust fuzzy controller. In this paper, the map is constructed on a two-dimensional occupancy grid. The sensor readings are fused into the map using D-S inference rule. Whenever the robot moves, it catches new information about the environment and replaces the old map with new one. With that process the robot can go wandering and finding the goal position. The usefulness of the proposed method is verified by a series of simulations. This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.