• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.255-261
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• 2022

Routing Protocol for Low-Power and Lossy Networks (RPLs) in Internet of Things (IoT) is currently one of the most popular wireless technologies for sensor communication. RPLs are typically designed for specialized applications, such as monitoring or tracking, in either indoor or outdoor conditions, where battery capacity is a major concern. Several routing techniques have been proposed in recent years to address this issue. Nevertheless, the expansion of the network lifetime in consideration of the sensors' capacities remains an outstanding question. In this research, aANN-CUCKOO based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IOT-RPL. The proposed method uses time constraints to minimise the distance between source and sink with the objective of a low-cost path. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. MATLAB software is used to simulate the proposed model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.8-17
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• 2005

This paper dealt with a kind of heterogeneous vehicle routing problem with known demand and time deadline of customers. The customers are supposed to have one of tight deadline and loose deadline. The demand of customers with tight deadline must be fulfilled in the deadline. However, the late delivery is allowed to customers with loose deadline. That is, the paper suggests a model to minimize total acquisition cost, total travel distance and total violation time for a fleet size and mix vehicle routing problem with time deadline, and proposes a heuristic algorithm for the model. The proposed algorithm consists of two phases, i.e. generation of an initial solution and improvement of the current solution. An initial solution is generated based on a modified insertion heuristic and iterative Improvement procedure is accomplished using neighborhood generation methods such as swap and reallocation. The proposed algorithm is evaluated using a well known numerical example.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.7
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• pp.1401-1407
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• 2002

This paper represents an investigation of the vehicle-to-vehicle distance control system using Hardware-in-the-Loop Simulation(HiLS). Control logic is primarily developed and tested with a specially equipped test vehicle. Establishment of an efficient and low cost development tool is a very important issue, and test vehicle approach is costly and time consuming. HiLS method is useful in the investigation of driver assistance and active safety systems. The HiLS system consists of a stepper motor for throttle control, a hydraulic brake system with an electronic vacuum booster, an electronic controller unit, a data logging computer which are used to save vehicle states and signals of actuator through a CAN and a simulation computer using mathematical vehicle model. Adaptation of a CAN instead of RS-232 Serial Interface for communication is a trend in the automotive industry. Since this environment is the same as a test vehicle, a control logic verified in laboratory can be easily transferred to a test vehicle.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.85-93
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• 2011

Many policies, such as transit-oriented development, encouraged use of bicycle and pedestrian, reduction of green house gas (GHG) and etc., have been deployed to support transport sustainability. Although various studies regarding GHG were presented, no one has yet adequately explained the behavior of travelers. This paper proposes a GHG emission model by highlighting its sensitivity, elasticity with regard to such travel cost as travel time, travel fare, and GHG pricing, introduced to reduce the amount of GHG in transportation system. For better estimation of GHG, the proposed model adopts (1) a production-constrained gravity model and (2) the travel distance from the origin and the destination (OD). The gravity model has a merit that it considers travel pattern between OD pairs. The model was tested with an example, and the promising results confirmed its validation and applications.

• The Journal of Bigdata
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• v.5 no.1
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• pp.155-180
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• 2020

It takes the most important role the problem of assigining vehicles and desigining optimal routes for each vehicle in order to enhance the logistics service level. While solving the problem, various cost factors such as number of vehicles, the capacity of vehicles, total travelling distance, should be considered at the same time. Although most of logistics service providers introduced the Transportation Management System (TMS), the system has the limitation which can not consider the practical constraints. In order to make the solution of TMS applicable, it is required experts revised the solution of TMS based on their own experience and intuition. In this research, different from previous research which have focused on minimizing the total cost, it has been proposed the methodology which can enhance the efficiency and fairness of asset utilization, simultaneously. First of all, it has been adopted the Cluster-First Route-Second (CFRS) approach. Based on the location of customers, we have grouped customers as clusters by using four different clustering algorithm such as K-Means, K-Medoids, DBSCAN, Model-based clustering and a procedural approach, Fisher & Jaikumar algorithm. After getting the result of clustering, it has been developed the optiamal vehicle routes within clusters. Based on the result of numerical experiments, it can be said that the propsed approach based on CFRS may guarantee the better performance in terms of total travelling time and distance. At the same time, the variance of travelling distance and number of visiting customers among vehicles, it can be concluded that the proposed approach can guarantee the better performance of assigning tasks in terms of fairness.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.328-333
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• 1994

This paper presents a real-time calculation method to generate the trajectory of robot manipulator for the purpose of avoiding collision. In order to model 3-D workspace, we use octree which has been used for fast collision detection. The levels of octree are used as the cost function to represent the distance between the manipulator and the obstacles. This criterion is not exact, but, due to this, we can obtain the approximate feasible trajectory extremely quickly. We will show the effectiveness of our method with some simulation examples. For example, the proposed method can solve a problem within 1 second on Intel 80486 processor running at 33 MHz. It has taken more than half an hour with one of the previously proposed methods.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.401-409
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• 2023

This study compared and analyzed the performance of a distributed area search algorithm and a distributed probability search algorithm based on information exchange between ships. The distributed algorithm is a method that can search for an optimal avoidance route based on information exchange between ships. In the distributed area search algorithm, only a ship with the maximum cost reduction among neighboring ships has priority, so the next expected location can be changed. The distributed stochastic search algorithm allows a non-optimal value to be searched with a certain probability so that a new value can be searched. A circular-based ship collision avoidance model was used for the ship-to-ship collision avoidance experiment. The experimental method simulated the distributed area search algorithm and the distributed stochastic search algorithm while increasing the number of ships from 2 to 50 that were the same distance from the center of the circle. The calculation time required for each algorithm, sailing distance, and number of message exchanges were compared and analyzed. As a result of the experiment, the DSSA(Distributed Stochastic Search Algorithm) recorded a 25%calculation time, 88% navigation distance, and 84% of number of message exchange rate compared to DLSA.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.410-415
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• 2005

Recently, most of the speaker verification systems are based on the pattern recognition approach method. And performance of the pattern-classifier depends on how to classify a variety of speakers' feature parameters. In order to classify feature parameters efficiently and effectively, it is of great importance to enlarge variations between speakers and effectively measure distances between feature parameters. Therefore, this paper would suggest the positively mixed model scheme that can enlarge inter-speaker variation by searching the individual model with world model at the same time. During decision procedure, we can maximize inter-speaker variation by using the proposed mixed model scheme. We also make use of a symbol probability weighting function in this system so as to reduce vector quantization errors by measuring symbol probability derived from the distance rate of between the world codebook and individual codebook. As the result of our experiment using this method, we could halve the Detection Cost Function (DCF) of the system from $2.37\%\;to\;1.16\%$.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.471-478
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• 2013

This paper proposes a black-box based test case generation method for Simulink/Stateflow model utilizing the RRT algorithm which is a method to efficiently solve the path planning for complicated systems. The proposed method in the paper tries to solve the reachability problem with the RRT algorithm, which has to be solved for black-box based test case generations. A major problem of the RRT based test case generation algorithms is that the cost such as running time and required memory size is too much for complicated Stateflow model. The typical RRT algorithm expands rapidly-exploring random tree (RRT) in a single state space. But the proposed method expands it in dynamic state space based on the state of Simulink model, consequently reducing the cost. In the paper, a new definition of RRT state space, a distance measure and a test case generation algorithm are proposed. The performance of proposed method is verified through the experiment against Stateflow model.