• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.9
• /
• pp.3266-3285
• /
• 2014

With limited computing and storage resources, many network applications of encryption algorithms require low power devices and fast computing components. CHESS-64 is designed by employing simple key scheduling and Data-Dependent operations (DDO) as main cryptographic components. Hardware performance for Field Programmable Gate Arrays (FPGA) and for Application Specific Integrated Circuits (ASIC) proves that CHESS-64 is a very flexible and powerful new cipher. In this paper, the security of CHESS-64 block cipher under related-key differential cryptanalysis is studied. Based on the differential properties of DDOs, we construct two types of related-key differential characteristics with one-bit difference in the master key. To recover 74 bits key, two key recovery algorithms are proposed based on the two types of related-key differential characteristics, and the corresponding data complexity is about $2^{42.9}$ chosen-plaintexts, computing complexity is about $2^{42.9}$ CHESS-64 encryptions, storage complexity is about $2^{26.6}$ bits of storage resources. To break the cipher, an exhaustive attack is implemented to recover the rest 54 bits key. These works demonstrate an effective and general way to attack DDO-based ciphers.

• Smart Structures and Systems
• /
• v.24 no.6
• /
• pp.733-744
• /
• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.77-87
• /
• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

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

• Electronics and Telecommunications Trends
• /
• v.36 no.2
• /
• pp.32-42
• /
• 2021

The rapid growth of deep-learning applications has invoked the R&D of artificial intelligence (AI) processors. A dedicated software framework such as a compiler and runtime APIs is required to achieve maximum processor performance. There are various compilers and frameworks for AI training and inference. In this study, we present the features and characteristics of AI compilers, training frameworks, and inference engines. In addition, we focus on the internals of compiler frameworks, which are based on either basic linear algebra subprograms or intermediate representation. For an in-depth insight, we present the compiler infrastructure, internal components, and operation flow of ETRI's "AI-Ware." The software framework's significant role is evidenced from the optimized neural processing unit code produced by the compiler after various optimization passes, such as scheduling, architecture-considering optimization, schedule selection, and power optimization. We conclude the study with thoughts about the future of state-of-the-art AI compilers.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3603-3618
• /
• 2022

The offloading method is important when there are multiple mobile nodes and multiple edge servers. In the environment, those mobile nodes connect with edge servers with different bandwidths, thus taking different time and energy for offloading tasks. Considering the system load of edge servers and the attributes (the number of instructions, the size of files, deadlines, and so on) of tasks, the energy-aware offloading problem becomes difficult under our mobile edge environment (MCE). Most of the past work mainly offloads tasks by judging where the job consumes less energy. But sometimes, one task needs more energy because the preferred edge servers have been overloaded. Those methods always do not pay attention to the influence of the scheduling on the future tasks. In this paper, first, we try to execute the job locally when the job costs a lower energy consumption executed on the MD. We suppose that every task is submitted to the mobile server which has the highest bandwidth efficiency. Bandwidth efficiency is defined by the sending ratio, the receiving ratio, and their related power consumption. We sort the task in the descending order of the ratio between the energy consumption executed on the mobile server node and on the MD. Then, we give a "suffrage" definition for the energy consumption executed on different mobile servers for offloading tasks. The task selects the mobile server with the largest suffrage. Simulations show that our method reduces the execution time and the related energy consumption, while keeping a lower value in the number of uncompleted tasks.

• Korean Journal of Construction Engineering and Management
• /
• v.8 no.1 s.35
• /
• pp.116-123
• /
• 2007

Recently orders of project in the field of oil & gas plant have been increased due to raising oil prices and consumptions. Domestic oil & gas companies are expanding into the Middle East area and all over the world. In the field of oil & gas plant, it is important to manage a technical control and a license that is occurred when developing the manufacturing processes and technologies. Because the oil & gas market has a lot of executions of the oversea construction, competitions between the advanced companies is more important. Therefore, we have to adopt systematic management style for achievements of the technical competitive power and the higher position. However, domestic oil & gas plant companies have not enough competitive powers of the license and the design phase. So, they are faced with difficulties of adopting the technology which is maximizing the effect of investments and scheduling a long-range plan. To achieve the technology management and the competitive power, this study proposes a long-range plan through the analysis of execution methods for technology management.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38B no.1
• /
• pp.46-53
• /
• 2013

Recently, intensive research has been performed for reducing video decoder energy consumption, especially based on DVFS (Dynamic Voltage and Frequency Scaling) technique. Our previous work [1] has proposed the optimal DVFS algorithm for energy reduction in video decoders. In spite of the mathematical optimality of the algorithm, the precondition of known frame decoding cycle/complexity limits its application to some realistic scenarios. This paper overcomes this limitation by frame data size-based estimation of frame decoding complexity. The proposed decoding complexity estimation method shows over 90% accuracy. And with this estimation method and buffer underflow margin of around 20% of frame size, almost same power consumption reduction performance as the optimal algorithm can be achieved.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.12
• /
• pp.1125-1134
• /
• 2013

Small cells such as pico or femto cells are promising as a solution to cope with higher traffic explosion and the large number of users. However, the users within small cells are likely to suffer severe inter-cell interference (ICI) from neighboring base stations (BSs). To tackle this, several papers suggest BS transmit power on/off control algorithms which increase edge user throughput. However, these algorithms require centralized coordinator and have high computational complexity. This paper makes a contribution towards presenting fully distributed and low complex joint BS on/off control and user scheduling algorithm (FDA) by selecting on/off pattern of BSs. Throughput the extensive simulations, we verify the performance of our algorithm as follows: (i) Our FDA provides better throughput performance of cell edge users by 170% than the algorithm without the ICI management. (ii) Our FDA catches up with the performance of optimal algorithm by 88-96% in geometric average throughput and sufficiently small gap in edge user throughput.

• Journal of Korea Multimedia Society
• /
• v.10 no.3
• /
• pp.348-357
• /
• 2007

One of the major issues in recent researches on wireless sensor networks is to reduce energy consumption of sensor nodes operating with limited battery power, in order to lengthen their lifespan. Among the researches, we are interested in the schemes in which a sensor node periodically turns on and off its radio and requires information on the time when its neighbors will wake up (or turn on). Clock synchronization is essential for wakeup scheduling in such schemes. This paper proposes three methods based on the asynchronous averaging algorithm for clock synchronization in sensor nodes which periodically wake up: (1) a fast clock synchronization method during an initial network construction period, (2) a periodic clock synchronization method for saving energy consumption, and (3) a decision method for switching the operation mode of sensor nodes between the two clock synchronization methods. Through simulation, we analyze maximum clock difference and the number of messages required for clock synchronization.