• Convergence Security Journal
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• v.23 no.4
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• pp.93-100
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• 2023

This paper presents a novel approach for assigning group keys within a dynamic swarm unmanned system environment. In this environment, multiple groups of unmanned systems have the flexibility to merge into a single group or a single unmanned system group can be subdivided into multiple groups. The proposed protocol encompasses two key steps: group key generation and sharing. The responsibility of generating the group key rests solely with the leader node of the group. The group's leader node employs a secret sharing scheme to fragment the group key into multiple fragments, which are subsequently transmitted. Nodes that receive these fragments reconstruct a fresh group key by combining their self-generated secret fragment with the fragment obtained from the leader node. Subsequently, they validate the integrity of the derived group key by employing the hash function. The efficacy of the proposed technique is ascertained through an exhaustive assessment of its security and communication efficiency. This analysis affirms its potential for robust application in forthcoming swarm unmanned system operations scenarios characterized by frequent network group modifications.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.94-100
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• 2008

Available transfer capability(ATC) quantifies the viable increase in real power transfer from one point to another in a power system. ATC calculation has predominantly focussed on steady-state viability. But ATC assessment with transient stability constraints has a dominant part in overall ATC calculation. ATC assessment requires a reputation of (n-1) security assessment with constraints of thermal limits, voltage stability and dynamic stability. An estimation of determinant contingency screening method is used for computing eigenvalue of Jacobian matrix. This paper proposed a methods to ATC calculation using energy function. Constraints is used thermal limits, voltage stability and transient stability.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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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.

• Proceedings of the KIEE Conference
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• 2005.07a
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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.

• Proceedings of the KIEE Conference
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• 2003.11a
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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.

• The Journal of Society for e-Business Studies
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• v.16 no.2
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• pp.129-142
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• 2011

Traditionally research in Service Oriented Architecture(SOA) security has focused primarily on exploiting standards and solutions separately. There exists no unified methodology for SOA security to manage risks at the enterprise level. It needs to analyze preliminarily security threats and to manage enterprise risks by identifying vulnerabilities of SOA. In this paper, we propose a metric-based vulnerability assessment method using dynamic properties of services in SOA. The method is to assess vulnerability at the architecture level as well as the service level by measuring run-time dependency between services. The run-time dependency between services is an important characteristic to understand which services are affected by a vulnerable service. All services which directly or indirectly depend on the vulnerable service are exposed to the risk. Thus run-time dependency is a good indicator of vulnerability of SOA.

• Smart Structures and Systems
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• v.31 no.1
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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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• v.23 no.9
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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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• v.23 no.8
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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.