• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.197-203
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• 2008

In this work, we present a novel genetic approach to solve the problem of combining power control and data rate transmission adjustment for the performance enhancement of the next generation CDMA system. We obtained the optimal solution of multi rate and power control problem by compromising slightly on SIR limit values. The proposed algorithm was able to handle many more users with comparable or faster convergent service. While this paper considered two kinds of fitness function such as maximizing the total transmission data rate and maximizing the acceptable mobiles of CDMA cellular network, the evaluation function combining these two cases or others can be also easily implemented. The simulation results showed the effectiveness and validity of our approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.178-185
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• 2013

As computer technology continues to develop, computer networks are now widely used. As a result, there are many new intrusion types appearing and information security is becoming increasingly important. Although there are many kinds of intrusion detection systems deployed to protect our modern networks, we are constantly hearing reports of hackers causing major disruptions. Since existing technologies all have some disadvantages, we utilize algorithms, such as the fuzzy C-means (FCM) and the support vector machine (SVM) algorithms to improve these technologies. Using these two algorithms alone has some disadvantages leading to a low classification accuracy rate. In the case of FCM, self-adaptability is weak, and the algorithm is sensitive to the initial value, vulnerable to the impact of noise and isolated points, and can easily converge to local extrema among other defects. These weaknesses may yield an unsatisfactory detection result with a low detection rate. We use a genetic algorithm (GA) to help resolve these problems. Our experimental results show that the combined GA and FCM algorithm's accuracy rate is approximately 30% higher than that of the standard FCM thereby demonstrating that our approach is substantially more effective.

• Clinical and Experimental Pediatrics
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• v.58 no.11
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• pp.407-414
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• 2015

Early-onset epileptic encephalopathies are one of the most severe early onset epilepsies that can lead to progressive psychomotor impairment. These syndromes result from identifiable primary causes, such as structural, neurodegenerative, metabolic, or genetic defects, and an increasing number of novel genetic causes continue to be uncovered. A typical diagnostic approach includes documentation of anamnesis, determination of seizure semiology, electroencephalography, and neuroimaging. If primary biochemical investigations exclude precipitating conditions, a trial with the administration of a vitaminic compound (pyridoxine, pyridoxal-5-phosphate, or folinic acid) can then be initiated regardless of presumptive seizure causes. Patients with unclear etiologies should be considered for a further workup, which should include an evaluation for inherited metabolic defects and genetic analyses. Targeted next-generation sequencing panels showed a high diagnostic yield in patients with epileptic encephalopathy. Mutations associated with the emergence of epileptic encephalopathies can be identified in a targeted fashion by sequencing the most likely candidate genes. Next-generation sequencing technologies offer hope to a large number of patients with cryptogenic encephalopathies and will eventually lead to new therapeutic strategies and more favorable long-term outcomes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1920-1937
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• 2015

Network environment has been under constant threat from both malicious attackers and inherent vulnerabilities of network infrastructure. Existence of such threats calls for exhaustive vulnerability analyzing to guarantee a secure system. However, due to the diversity of security hazards, analysts have to select from massive alternative hardening strategies, which is laborious and time-consuming. In this paper, we develop an approach to seek for possible hardening strategies and prioritize them to help security analysts to handle the optimal ones. In particular, we apply a Risk Flow Attack Graph (RFAG) to represent network situation and attack scenarios, and analyze them to measure network risk. We also employ a multi-objective genetic algorithm to infer the priority of hardening strategies automatically. Finally, we present some numerical results to show the performance of prioritizing strategies by network risk and hardening cost and illustrate the application of optimal hardening strategy set in typical cases. Our novel approach provides a promising new direction for network and vulnerability analysis to take proper precautions to reduce network risk.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.1-10
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• 2006

Load redistribution algorithm is a critical factor in computer system. In a receiver-initiated load redistribution algorithm, receiver(underloaded processor) continues to send unnecessary request messages for load transfer until a sender(overloaded processor) is found while the system load is light. Therefore, it yields many problems such as low CPU utilization and system throughput because of inefficient inter-processor communications until the receiver receives an accept message from the sender in this environment. This paper presents an approach based on genetic. algorithm(GA) for dynamic load redistribution in heterogeneous distributed systems. In this scheme the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages.

• Clinical and Experimental Pediatrics
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• v.63 no.6
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• pp.195-202
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• 2020

Developments in next-generation sequencing (NGS) techogies have assisted in clarifying the diagnosis and treatment of developmental delay/intellectual disability (DD/ID) via molecular genetic testing. Advances in DNA sequencing technology have not only allowed the evolution of targeted panels but also, and more currently enabled genome-wide analyses to progress from research era to clinical practice. Broad acceptance of accuracy-guided targeted gene panel, whole-exome sequencing (WES), and whole-genome sequencing (WGS) for DD/ID need prospective analyses of the increasing cost-effectiveness versus conventional genetic testing. Choosing the appropriate sequencing method requires individual planning. Data are required to guide best-practice recommendations for genomic testing, regarding various clinical phenotypes in an etiologic approach. Targeted panel testing may be recommended as a firsttier testing approach for children with DD/ID. Family-based trio testing by WES/WGS can be used as a second test for DD/ID in undiagnosed children who previously tested negative on a targeted panel. The role of NGS in molecular diagnostics, treatment, prediction of prognosis will continue to increase further in the coming years. Given the rapid pace of changes in the past 10 years, all medical providers should be aware of the changes in the transformative genetics field.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.788-793
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• 2015

This paper proposes a path planning algorithm to reduce the inspection time of AOI (Automatic Optical Inspection) machines for SMT (Surface Mount Technology) in-line system. Since the field-of-view of the camera attached at the machine is much less than the entire inspection region of board, the inspection region should be clustered to many groups. The image acquisition time depends on the number of groups, and camera moving time depends on the sequence of visiting the groups. The acquired image is processed while the camera moves to the next position, but it may be delayed if the group includes many components to be inspected. The inspection delay has influence on the overall job time of the machine. In this paper, we newly considers the inspection delay time for path planning of the inspection machine. The unified approach using genetic algorithm is applied to generates the groups and visiting sequence simultaneously. The chromosome, crossover operator, and mutation operator is proposed to develop the genetic algorithm. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1218-1227
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• 2004

Multi-domain engineering systems include electrical, mechanical, hydraulic, pneumatic, and thermal components, making it difficult to design a system because of their complexity and inter domain nature. In order to obtain an optimal design, a unified design approach for each domain and an automated search method are required. This paper suggests a method for automatically synthesizing designs for multi-domain systems using the combination of bond graph that is domain independent and genetic programming that is well recognized as a powerful tool for open-ended search. To investigate the effect of proposed approach, an eigenvalue design problem is tested for some sample target sets of eigenvalues with different embryos.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.1-12
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• 2009

In recent years, integration of new distributed generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. This paper presents a dynamic methodology of optimal allocation and sizing of DG units for a given practical distribution network, so that the cost of active power can be minimized. The approach proposed is based on a combined Genetic/Fuzzy Rules. The genetic algorithm generates and optimizes combinations of distributed power generation for integration into the network in order to minimize power losses, and in second step simple fuzzy rules designs based upon practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensator (SVC, STATCOM) in order to improve the system loadability. This proposed approach is implemented with the Matlab program and is applied to small case studies, IEEE 25-Bus and IEEE 30-Bus. The results obtained confirm the effectiveness in sizing and integration of an assigned number of DG units.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).