• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.49-54
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• 2021

The purpose of this study is to identify trends in T commerce and further propose ways to improve customer data-based services and development models for changes in broadcasting screens with the expansion of IPTV subscribers. Implementing a customized shopping model like mobile through TV media and improving customer satisfaction will reduce customer departures and provide a more convenient shopping environment through large screens. We would like to learn about the current status and problems of T commerce broadcasting and explain some technically validated models (channel-in-channel, AI speaker) and talk about improvement of legal (broadcasting and Internet multimedia business law) constraints.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.157-162
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• 2023

To NP-complete 3-SAT problem, this paper proposes a O(nm) polynomial time algorithm, where n is the number of literals and m is the total frequency of all literals in equation f. The algorithm firstly decides a truth value of a literal in sequence of previously-set priority. The priority order is as follows: a literal whose occurrence in a clause is 1(k=1), a literal which is k≥2 and whose truth value is either 0 or 1, and a literal with the minimum frequency. Then, literals whose truth value is determined are then deleted from clause T and the remaining clauses. This process is repeated l times, the number of literals. As a result, the proposed algorithm has been successful in accurately determining the satisfiability of a given equation f and in deciding the truth value of all the literals. This paper, therefore, provides not only a linear-time algorithm as a viable solution to the SAT problem, but also a basis for solving the P versus NP problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.95-101
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• 2022

The two-sided assembly line balancing problem is a kind of NP-hard problem. This problem primarily can be solved metaheuristic method. This paper suggests parallel clustering algorithm that each left and right-sided workstation assigned by operations with T_i = c^* ± α < c, c^* = ${\lceil}$W/m^*${\rceil}$ such that M^* = ${\lceil}$W/c${\rceil}$ for precedence diagram of two-sided assembly line with total complete time W and cycle time c. This clustering performs forward direction from left to right or reverse direction from right to left. For the 4 experimental data with 17 cycle times, the proposed algorithm can be obtain the minimum number of workstations m^* and can be reduce the cycle time to T_max < c then metaheuristic methods. Also, proposed clustering algorithm maximizes the line efficiency and minimizes the variance between workers operation times.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.177-183
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• 2022

There is no known polynomial time algorithm for QAP that is a NP-complete problem. This paper suggests O(n²) polynomial time algorithm for random type quadratic assignment problem (QAP). The proposed algorithm suggests incremental augmenting matching strategy that is to set the matching set M={(l_i,f_j)} from l_i with minimum sum of distance in location matrix L and f_j with maximum sum of quantity in facility matrix F, and incremental augmenting of matching set M from M to l_i with minimum sum of distance and to f_j with maximum sum of quantity. Finally, this algorithm performs swap strategy that is to reflect the complex correlations of distances in locations and quantities in facilities. For the experimental data, this algorithm, in spite of O(n²) polynomial time algorithm, can be improve the solution than genetic algorithm a kind of metaheuristic method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.95-102
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• 2022

This paper proposed a linear time algorithm for a three-partition problem(TPP) in which a polynomial time algorithm is not known as NP-complete. This paper proposes a backtracking method that improves the problems of not being able to obtain a solution of the MM method using the sum of max-min values and third numbers, which are known polynomial algorithms in the past. In addition, the problem of MM applying the backtracking method was improved. The proposed algorithm partition the descending ordered set S into three and assigned to the forward, backward, and best-fit allocation method with maximum margin, and found an optimal solution for 50.00%, which is 5 out of 10 data in initial allocation phase. The remaining five data also showed performance to find the optimal solution by exchanging numbers between surplus boxes and shortage boxes at least once and up to seven times. The proposed algorithm that performs simple allocation and exchange optimization with less O(k) linear time performance complexity than the three-partition m=n/3 data, and it was shown that there could be a polynomial time algorithm in which TPP is a P-problem, not NP-complete.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.185-191
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• 2023

To the exact cover problem that remains NP-complete to which no polynomial time algorithm is made available, this paper proposes a linear time algorithm that yields an optimal solution. The proposed algorithm makes use of the set cover problem's major feature which states that "no identical element shall be included in more than one covering set". To satisfy this criterion, the proposed algorithm initially selects a subset with the minimum cardinality and deletes those that contain the cardinality identical to that of the selected subset. This process is repeatedly performed on remaining subsets until the final solution is obtained. Provided that the solution is unattainable, it selects subsets with the maximum cardinality and repeats the same process. The proposed algorithm has not only obtained the optimal solution with ease but also proved its wide applicability on N-queens problems, hence disproving the NP-completeness of the exact cover problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.73-78
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• 2023

The dining philosophers problem(DPP) is that five philosophers sit around a round table and eat spaghetti(or noodles) together, where they must have a pair of chopsticks(two) on both sides of them to eat, and if all philosophers have one chopstick on the right, no one can eat because the deadlock occurs. Deadlocks are a problem that frequently occur in parallel systems, and most current operating systems(OS) cannot prevent it. This paper proposes a silver bullet that causes no deadlock in an OS where all processors of 2≤n≤∞ have multiple parallel processing capabilities. The proposed method is a group round-robin method in which ⌊n/2⌋ odd processors form a group and perform simultaneously, and shift right to the next processor when execution ends. The proposed method is to perform two times for even processors, three times for odd processors per one round-robin. If the proposed method is performed n times, even-numbered processors perform n/2 times and odd-numbered processors perform (n-1)/2-times.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.165-170
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• 2023

This paper proposes an algorithm that can obtain a solution with linear time for a set cover problem(SCP) in which there is no polynomial time algorithm as an NP-complete problem so far. Until now, only heuristic greed algorithms are known to select sets that can be covered to the maximum. On the other hand, the proposed algorithm is a competitive algorithm that applies an inclusion-exclusion principle rule to N nodes up to 2nd or 3rd in the maximum number of elements to obtain a set covering all k nodes, and selects the minimum cover set among them. The proposed algorithm compensated for the disadvantage that the greedy algorithm does not obtain the optimal solution. As a result of applying the proposed algorithm to various application cases, an optimal solution was obtained with a polynomial time of O(kn²).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.167-173
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• 2023

The cash flow matching problem(CFMP) aims to minimize the initial investment by paying the total amount due for the T-year in principal and interest of bonds or bank deposits without paying the full amount in cash. Linear programming(LP) is the only known way to solve CFMP. The linear programming method is a problem that optimizes T linear functions, and it cannot be solved by handwriting, so LINGO, which is a solution to the linear programming method, is used. This paper proposes an algorithm that obtains the solution of CFMP solely by handwriting without the help of LINGO. The proposed algorithm determines the amount of bond purchases by covering payments until the previous year of the next maturity bond in the order that the maturity date falls from the longest to the short term. In addition, until the year before the maturity of the shortest maturity bond, the amount of deposit covered by the principal and interest of the bank deposit was determined. As a result of applying the proposed algorithm to two experimental data, it was shown that more accurate results can be obtained compared to the linear programming method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.75-81
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• 2022

Autonomous vehicles, which can dramatically solve the lack of parking spaces, are making great progress through deep reinforcement learning. Activation functions are used for deep reinforcement learning, and various activation functions have been proposed, but their performance deviations were large depending on the application environment. Therefore, finding the optimal activation function depending on the environment is important for effective learning. This paper analyzes 12 functions mainly used in reinforcement learning to compare and evaluate which activation function is most effective when autonomous vehicles use deep reinforcement learning to learn parking. To this end, a performance evaluation environment was established, and the average reward of each activation function was compared with the success rate, episode length, and vehicle speed. As a result, the highest reward was the case of using GELU, and the ELU was the lowest. The reward difference between the two activation functions was 35.2%.