• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4044-4062
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• 2016

We investigate the optimization of energy consumption in Mobile Cloud environment in this paper. In order to optimize the energy consumed by the CPUs in mobile devices, we put forward using the asymptotic time complexity (ATC) method to distinguish the computational complexities of the applications when they are executed in mobile devices. We propose a multi-scale scheme to quantize the channel gain and provide an improved dynamic transmission scheduling algorithm when offloading the applications to the cloud center, which has been proved to be helpful for reducing the mobile devices energy consumption. We give the energy estimation methods in both mobile execution model and cloud execution model. The numerical results suggest that energy consumed by the mobile devices can be remarkably saved with our proposed multi-scale scheme. Moreover, the results can be used as a guideline for the mobile devices to choose whether executing the application locally or offloading it to the cloud center.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.333-336
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• 1991

In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.

• Architectural research
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• v.11 no.2
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• pp.43-49
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• 2009

Since the late 1980s, the schedule technique applied to the construction industry around the world has rapidly changed from the traditional ADM (Arrow Diagramming Method) to the PDM (Precedence Diagramming Method) technique. The main reason for this change is to overcome the limits and inconveniences of the traditional ADM technique. The time-cost trade-off is one of the core scheduling techniques to establish the best optimized combination plan in terms of a relationship between the cost and schedule. However, most of the schedule-related textbooks and research papers have discussed and proposed applications of a time-cost trade-off technique based only on the Finish to Start relationship. Therefore, there are almost no consideration and discussion of problems or restrictions that emerge when the time-cost trade-off technique is applied to the PDM network that has overlapping relationships. This paper proposes the lead-time adjustment method as a methodology for overcoming some restrictions that are encountered when the time-cost trade-off technique is applied to the overlapping relationships of the PDM network.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.9
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• pp.520-529
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• 2002

This paper presents an approach for scheduling network messages with real-time dynamic algorithms. We present the method that calculates an intermediate relative deadline of the message based on the EDF(Earliest Deadline First) scheduling policy. We adjust the slack of message by using this intermediate relative deadline to allocate a priority of message. The priority of the message can be determined accurately by using the slack that calculates in our approach, which increases the schedulability efficiency of the message. As a result, we reduce the worst-case response time and improve the guarantee ratio of real-time messages. Also, we describe the analysis method to check the schedulability on message sets, and show the efficiency of our approach by comparing the results of the DM(Deadline Monotonic) approach and the existing EDF approach with that of the improved EDF in our approach through the simulation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.1
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• pp.63-68
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• 2015

Pattern layout design is very important to the automation of apparel industry. Until now, the genetic algorithm and Tabu search method have been applied to layout design automation. With the genetic algorithm and Tabu search method, the obtained values are not always consistent depending on the initial conditions, number of iterations, and scheduling. In addition, the selection of various parameters for these methods is not easy. This paper presents a hybrid search method that uses a neural network and simulated annealing to solve these problems. The layout of pattern elements was optimized to verify the potential application of the suggested method to apparel pattern layout design.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.10
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• pp.29-34
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• 2011

We present a network coding scheme which is designed for improving throughput in multi-hop wireless network with multi-radio multi-channel. The co-channel interference and unreliability of wireless transmissions cause the wireless network to reduce throughput. In wireless network, multi-radio multi-channel technology shows benefit to cut down channel interferences and contentions. And network coding can reduce the complexity of scheduling and improve throughput by increasing usage of links in wireless network. In this paper, we propose a method of channel assignment and transmission scheduling in intra-session network coding that efficiently improve throughput for multi-hop wireless network by using mathematical modeling and linear programming. Moreover, we evaluate the performance of the intra-session network coding scheme by using AMPL with CPLEX. The simulation results show that intra-session network coding can achieve better throughput than traditional routing.

• ETRI Journal
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• v.38 no.5
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• pp.885-895
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• 2016

The lowest power mode (passive-standby mode) was proposed for reducing the power consumption of set-top boxes in a standby state when not receiving content. However, low-power set-top boxes equipped with the lowest power mode have been rarely commercialized because of their low-quality user experience. In the lowest power mode, they deactivates almost all of operational modules and processes, and thus require dozens of seconds for activation latency (that is, the latency for activating all modules of the set-top boxes in a standby state). They are not even updated in a standby state because they deactivate their network interfaces in a standby state. This paper proposes an adaptive standby mode scheduling method for improving the user experience of such boxes. Set-top boxes using the proposed method can analyze the activation pattern and find the frequently used time period (that is, when the set-top boxes are frequently activated). They prepare for their activation during this frequently used time period, thereby reducing the activation latency and enabling their update in a standby state.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.

• IE interfaces
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• v.11 no.2
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• pp.13-24
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• 1998

A distribution system is composed of multiple levels from a producer to customers, and it's objective is to supply customers with goods timely with a prescribed level of quality at a minimum cost. For the installation and operation of multi-echelon distribution system, DRP(Distribution Resource Planning) is widely used. However, because of the characteristic difference of material flow dynamic of each distribution center, it is almost impossible to get the optimal distribution scheduling. In this paper, an improved DRP method to schedule multi-echelon distribution network is proposed so that the lot-size and order point is dynamically obtained to meet the change of demand rate and timing. The experiment is done with various demand pattern, forecast errors of demand and lead times of central distribution center. The proposed method is compared with traditional statistical approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.65-67
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• 2013

The recent high-performance network equipment is required, and also require high network bandwidth utilization. It is a trend to develop increasingly using multi-core processors for high-performance network servers. Propose a method to improve the performance of the network sub-system, considering the characteristics of multi-core as a way to improve these high-performance and high network throughput. In this paper, we confirm through experiments on how to improve the communication performance, optimize performance and take full advantage of multi-core by Network communication process to improve the performance of the multi-core processor architecture, the process of concentration, the overhead for each core, based on network traffic according to the interrupt affinity in this process to determine the optimal core to give. The experiments were implemented in the Linux kernel, and experiments to improve the network throughput up to 30%, bringing reduces the Linux communication process to improve the performance of the processor overhead of up to 10%.