• Archives of Plastic Surgery
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• v.41 no.5
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• pp.535-541
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• 2014

Background Patients having unilateral breast reconstruction often require a second stage procedure on the contralateral breast to improve symmetry. In order to provide immediate symmetry and minimize the frequency and extent of secondary procedures, we began performing simultaneous contralateral balancing operations at the time of initial reconstruction. This study examines the indications, safety, and efficacy of this approach. Methods One-hundred and two consecutive breast reconstructions with simultaneous contralateral balancing procedures were identified. Data included patient age, body mass index (BMI), type of reconstruction and balancing procedure, specimen weight, transfusion requirement, complications and additional surgery under anesthesia. Unpaired t-tests were used to compare BMI, specimen weight and need for non-autologous transfusion. Results Average patient age was 48 years. The majority had autologous tissue-only reconstructions (94%) and the rest prosthesis-based reconstructions (6%). Balancing procedures included reduction mammoplasty (50%), mastopexy (49%), and augmentation mammoplasty (1%). Average BMI was 27 and average reduction specimen was 340 grams. Non-autologous blood transfusion rate was 9%. There was no relationship between BMI or reduction specimen weight and need for transfusion. We performed secondary surgery in 24% of the autologous group and 100% of the prosthesis group. Revision rate for symmetry was 13% in the autologous group and 17% in the prosthesis group. Conclusions Performing balancing at the time of breast reconstruction is safe and most effective in autologous reconstructions, where 87% did not require a second operation for symmetry.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.65-73
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• 2018

Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.543-548
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• 2015

Recently, volume of data has been growing dramatically along with the growth of social media and digital devices. However, the existing disk-based distributed file systems have limits to their performance of data processing or data access, due to I/O processing costs and bottlenecks. To solve this problem, the caching technique is being used to manage data in the memory. In this paper, we propose a cache management scheme to handle load balancing in a distributed memory environment. The proposed scheme distributes the data according to the memory size, n distributed environments with different memory sizes. If overloaded nodes occur, it redistributes the the access time of the caching data. In order to show the superiority of the proposed scheme, we compare it with an existing distributed cache management scheme through performance evaluation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.176-179
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• 2002

In recent years, an increasing amount of computer network research has focused on the problem of cluster system in order to achieve higher performance and lower cost. The load unbalance is the major defect that reduces performance of a cluster system that uses parallel program in a form of SPMD (Single Program Multiple Data). Also, the load unbalance is a problem of MPP (Massive Parallel Processors), and distributed system. The cluster system is a loosely-coupled distributed system, therefore, it has higher communication overhead than MPP. Dynamic load balancing can solve the load unbalance problem of cluster system and reduce its communication cost. The cluster systems considered in this paper consist of P heterogeneous nodes connected by a switch-based network. The master node can predict the average execution time of tasks for each slave node based on the information from the corresponding slave node. Then, the master node redistributes remaining tasks to each node considering the predicted execution time and the communication overhead for task migration. The proposed dynamic load balancing uses execution time prediction to optimize the task redistribution. The various performance factors such as node number, task number, and communication cost are considered to improve the performance of cluster system. From the simulation results, we verified the effectiveness of the proposed dynamic load balancing algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.336-341
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• 2015

This paper proposes a balancing and driving control system for a Mecanum wheel ball robot which has a two axis structure and four motors. The inverted pendulum control method is adopted to maintain the balance of the ball robot while it is driving. For the balancing control, an anon-model-based controller has been designed to control the device simply without the need of a complex formula. All the gains of the controller are heuristically adjusted during the experiments. The tilt angle is measured by IMU sensors, which is used to generate the control input of the roll and pitch controller to make the tilt angle zero. For the driving control, the PID control algorithm has been adopted with angles of the wheels and the encoder data. The performance of the designed control system has been verified through the real experiments with the suggested ball robot.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.54-60
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• 2002

A mini turbo-pump having 44mm diameter impeller for hydraulic power control have been tested to evaluate hydraulic performance and losses. The characteristics of the losses such as mechanical, friction, balancing rib losses were investigated. The investigation revealed that the friction loss is relatively large but the balancing rib loss small. It was found that the hydraulic efficiency of the pump at design point is very low($27\%$) due to low specific speed and large friction losses. A computational fluid dynamics(CFD) method also has been utilized for performance prediction of the mini turbo-pump to compare the computed results with the test data.

• Journal of Family Resource Management and Policy Review
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• v.14 no.1
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• pp.1-17
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• 2010

This study aimed to develop the educational program for the Healthy Families Center. This study focused on married, working women and the difficulties they face balancing work and family. The data were collected from interviews with eight married, working women, six professionals from the Healthy Families Center. The program is composed of three parts. The first looks at the identities of married, working women. The second part focuses on the everyday lives of married, working women and the experiences they have balancing work and housewife duties. The third part outlines strategies for the balancing of work and family.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.205-206
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• 2008

We propose load balancing algorithm based on cross layer designing for MIMO OFDM system. When there are many users using data service, base station(BS) should distribute traffic. Moreover, cross layer design gives benefit managing radio resource and network bandwidth management. Proposed cross layer load balancing technique manages both BS's bandwidth allocation and MS’s power control. One BS request bandwidth to other BSes and other BSes reduce each bandwidth. And BSes reduce power of sub carriers for reserving available bandwidth of backhaul. MSes that didn't get service can be served by obtaining bandwidth from other BSes. The simulation result shows more users can be served and cell throughput was increased