• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.1
• /
• pp.45-61
• /
• 2022

Despite of the consensus on the critical role of CEO and slack resources for strategic decision making, how they affect in the selection of strategic alliance type is limited. This study investigated the effect of CEO's experiential attributes and the venture firms' slack resource on the selection of strategic alliance type. To this end, this study used multi-variate logistic regression analysis with 1,813 Korean venture firms. The findings indicated that higher education level and large firm experience of CEO positively contributed to form an explorative alliance. And these two experiential attributes has negative effects on the probability of exploitative alliance formation. On the other hand, the entrepreneurial experience has no effect on the selection of strategic alliance type. This study also investigated the effect of slack resource - available slack, recoverable slack, and potential slack-. The more venture firms have available and potential slack, the higher probability of pursuing an explorative alliance. In addition, recoverable slack of venture firms has negative effect only on the selection of explorative alliance. The results of this study are expected to contribute the literatures of strategic management and venture firms by illustrating which CEO and firm-level factors affect the selection of strategic alliance type. This study also extends recent effort to better understand the selection of strategic alliance type with upper echelons theory and slack resource. And this study suggests implications that can increase the probability of successful decision making by venture firms in selection of strategic alliance type.

• Architectural research
• /
• v.4 no.1
• /
• pp.45-52
• /
• 2002

Selection procedures of earth retention systems are increasingly complex and directly related to the serviceability of the retaining structure selection systems since significant changes in earth retention technology motivates the review of design, and selection processes of earth retaining structures. Collection and classification of retaining structure selection knowledge are key issues because two expert groups, geotechnical and structural engineers, are mainly involved in the retaining structure selection. The course of natural tendency of expert knowledge are investigated considering the decision factors. The decision factors for selecting retaining structures are divided into four categories: application of the structure, and spatial, behavior, and economic constraints.

• ETRI Journal
• /
• v.36 no.4
• /
• pp.609-616
• /
• 2014

Even though many computational methods (recursive formulae) for blocking probabilities in finite-capacity M/D/1 queues have already been produced, these are forms of transforms or are limited to single-node queues. Using a distinctly different approach from the usual queueing theory, this study introduces explicit (transform-free) formulae for a blocking probability, a stationary probability, and mean sojourn time under either production or communication blocking policy. Additionally, the smallest buffer capacity subject to a given blocking probability can be determined numerically from these formulae. With proper selection of the overall offered load ${\rho}$, the approach described herein can be applicable to more general queues from a computational point of view if the explicit expressions of random vector $D_n$ are available.

• International Journal of Internet, Broadcasting and Communication
• /
• v.9 no.4
• /
• pp.51-58
• /
• 2017

This paper deals with the improvement of cluster head selection method in L-SEP for heterogeneous nodes among hierarchical routing protocols of wireless sensor network. Wireless sensor networks are classified into homogeneous and heterogeneous network. In heterogeneous network, SEP, L-SEP are mainly used because cluster head selection probability is different depending on node type. But, since protocol based on SEP has different cluster head selection probabilities depending on the node type, clusters that transmit data inefficiently can be formed. to improve this, it is necessary to select the cluster head that minimizes the transmission distance of member node and the cluster head. Therefore, we propose a protocol that improve the cluster head selection method.

• International journal of advanced smart convergence
• /
• v.10 no.1
• /
• pp.56-61
• /
• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.7
• /
• pp.1-7
• /
• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Korean Journal of Remote Sensing
• /
• v.34 no.1
• /
• pp.151-166
• /
• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39B no.11
• /
• pp.803-809
• /
• 2014

In this paper, we investigate physical layer security in a cooperative cognitive radio networks (CRN) with a relay selection in the presence of a primary user and an eavesdropper. To protect the CRN from wiretapping by the eavesdropper, we propose employing an opportunistic relay selection scheme and multiple antennas at the destination that work based on the availability of channel state information at the receivers. Under these configurations, we derive an exact closed-form expression for the secrecy outage probability of the CRN, and also derive an asymptotic probability. Numerical results will be presented to verify the analysis.

• Membrane and Water Treatment
• /
• v.8 no.2
• /
• pp.137-147
• /
• 2017

A water treatment utility in South Korea operates a large system of pressurized hollow fiber membrane (PHFM) modules. The optimal selection of membrane module for the full scale plant was critical issue and carried out using Risk-based Life Cycle Cost (RbLCC) analysis based on the historical data of operation and maintenance. The RbLCC analysis was used in the process of decision-making for replacing aged modules. The initial purchasing cost and the value at risk during operation were considered together. The failure of modules occurs stochastically depending on the physical deterioration with usage over time. The life span of module was used as a factor for the failure of Poisson's probability model, which was used to obtain the probability of failure during the operation. The RbLCC was calculated by combining the initial cost and the value at risk without its warranty term. Additionally, the properties of membrane were considered to select the optimum product. Results showed that the module's life span in the system was ten years (120 month) with safety factor. The optimum product was selected from six candidates membrane for a full scale water treatment facility. This method could be used to make the optimum and rational decision for the operation of membrane water purification facility.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.17-26
• /
• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.