• 융합보안논문지
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• 제23권4호
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• pp.93-100
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• 2023

본 논문은 여러 개의 무인체계 그룹이 하나의 그룹으로 통합되거나 하나의 무인체계 그룹이 여러 개의 그룹으로 분리될 수 있는 동적인 군집 무인체계 환경에서의 그룹키 할당 기법을 제안한다. 제안하는 프로토콜은 그룹키 생성 단계와 그룹키 공유단계로 구성된다. 그룹키 생성에는 그룹의 대표 노드만이 참여하며, 그룹 대표 노드는 비밀분산기법을 이용하여 그룹키를 여러 조각으로 분할하여 전달한다. 이를 수신한 노드들은 자신이 생성한 그룹키의 비밀 조각과 대표노드로 부터 수신한 조각들을 통해 새로운 그룹키를 개별적으로 추출하고 해시함수를 이용하여 추출된 그룹키의 무결성을 검증하다. 제안하는 기법의 성능을 보안성 및 통신효율성 측면에서 분석하여 네트워크 그룹이 매우 동적으로 변화하는 미래 군집 무인체계 운용에 효과적으로 적용될 수 있음을 확인한다.

• 조명전기설비학회논문지
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• 제22권2호
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• pp.94-100
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• 2008

가용송전용량(ATC)은 계통내의 한 지역에서 다른 지역까지 실제 전력을 증가시키는 것이다. 지금까지 ATC 계산은 대부분 정상상태에서 실행가능성을 주로 고려하여 계산되어 왔다. 하지만 ATC 평가시 과도안정도로 제약된 ATC 계산은 매우 중요한 부분이다. ATC 평가시에는 제약조건으로 열적용량, 전압 및 과도안정도로 제약된 상정사고(n-1)시 안전도 평가가 요구된다. 본 논문은 자코비안 행렬의 고유치를 이용하여 상정사고 우선순위를 선정하였고, 에너지 함수법을 이용하여 선로의 열적용량, 전압안정도 및 과도안정도를 고려한 ATC를 계산하였다.

• The Journal of Asian Finance, Economics and Business
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• 제6권4호
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• pp.287-295
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• 2019

The study aims to build a model for evaluating the effectiveness of activities and the effectiveness of financial investments in high-tech industries in Kazakhstan. The development of high-tech industries plays an important role in the economic growth of a country. In this regard, it is relevant to study the effectiveness of financing the most important industry in Kazakhstan. The development of the high-tech sector ensures the efficient functioning of the national innovation system. High-tech enterprises are one of the competitive sectors that allow us to develop and implement leading-edge innovations with the goal of their subsequent commercialization domestically and abroad. The author defines the multicriteria of efficiency in a knowledge-based economy associated with achieving an economic effect with multivariate correlation of results with costs. A multivariate dynamic model, an integral indicator of performance, an integral indicator of cost-effectiveness is proposed. The assessment of the effectiveness of financial costs and performance indicators in all regions of Kazakhstan have the positive dynamics of indicators, as well as a high economic effect. The results of the study can be applied in regional management to adequately assess the effectiveness of high-tech organizations and the effectiveness of financial investments, contribution to ensuring the economic security of the region.

• 전기학회논문지
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• 제57권3호
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• pp.354-362
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• 2008

This paper presents the new method of power system transient stability simulation, which combines the desirable features of both the time domain technique based on OOP(Object-oriented Programming) and the direct method of transient stability analysis using detailed generator model. OOP is an alternative to overcome the problems associated with the development, maintenance and update of large software by electrical utilities. Several papers have already evaluated this approach for power system applications in areas such as load flow, security assessment and graphical interface. This paper applied the object-oriented approach to the problem of power system dynamics simulation. The modeling method is that each block of dynamic system block diagram is implemented as an object and connected each other. In the transient energy method, the detailed synchronous generator model is so-called two-axis model. For the excitation model, IEEE type1 model is used. The developed mothed was successfully applied to New England Test System.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.136-139
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• 2005

On-line dynamic security assessment is becoming more and more important for the stable operation of power systems as load level increases. The necessity is getting apparent under Electricity Market environments due to more various operating conditions. Fast transient stability analysis tool is required for contingency selection. The TEF(Transient Energy Function) method is a good candidate for this purpose. The clustering of critical generators is crucial for the precise and fast calculation of energy margin. In this paper, we propose a new method for fast decision of mode of instability by using stability indices. Case study shows very promising results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전력기술부문
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• pp.34-37
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• 2003

The frequency of power system will change when the load-generation equilibrium is disturbed. Insufficiency of generation from the imbalance between load and generation decreases the power system frequency. In case of the severe emergency, the under frequency load shedding scheme is applied for the power system defense plan. In this paper, we analyzed the dynamic characteristics of under frequency load shedding using new Transient Security Assessment Tool ; TSAT. We applied the actual UFLS scheme to these studies and considered the possible contingency.

• 한국전자거래학회지
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• 제16권2호
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• pp.129-142
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• 2011

현재까지 서비스지향 아키텍처(SOA) 보안은 개별 표준들과 솔루션에 의한 대응에 집중해있을 뿐 전사적인 위험관리 차원의 통합적인 방법론은 부족한 실정이다. 따라서 SOA 보안 취약점 식별을 통한 다양한 보안 위협들에 대한 사전 분석과 대책 수립이 필요하다. 이를 위해 본 논문에서는 SOA 취약성을 정량적으로 분석하기 위한 SOA 동적 특성을 이용한 메트릭 기반의 취약성 평가 방법을 제안한다. SOA를 구성하는 서비스들 사이의 실행시간 종속성을 측정하여 서비스와 아키텍처 수준의 취약성을 평가한다. 서비스 사이의 실행시간 종속성은 한 서비스가 취약할 때 다른 서비스들이 얼마나 영향을 받게 되는지를 분석하기 위해 사용되는 중요한 특징이다. 한 서비스가 공격에 노출될 때 그 서비스에 종속된 서비스들도 역시 공격가능성이 높아진다. 따라서 실행시간 종속성은 SOA 아키텍처 수준의 취약성에 대한 지표로 활용할 수 있다.

• Smart Structures and Systems
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• 제31권1호
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.