• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.59-67
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• 2016

With the enhancement of built-in communication capabilities in various meters and wearable devices, which implies Internet of things (IoT), the demand of small-area embedded processors has increased. In this paper, we introduce a small-area 32-bit pipelined processor, Juno, which is available in the field of IoT. Juno is an EISC (Extendable Instruction Set Computer) machine and has a 2/3-stage pipeline structure to reduce the data dependency of the pipeline. It has a simple pipeline controller which only controls the program counter (PC) and two pipeline registers. It offers $32{\times}32=64$ multiplication, 64/32=32 division, $32{\times}32+64=64$ MAC (multiply and accumulate) operations together with 32*32=64 Galois field multiplication operation for encryption processing in wireless communications. It provides selective inclusion of these algebraic logic blocks if necessary in order to reduce the area of the overall processor. In this case, the gate count of our integer core amounts to 12k~22k and has a performance of 0.57 DMIPS/MHz and 1.024 Coremark/MHz.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• Korean Journal of Ichthyology
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• v.36 no.1
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• pp.1-9
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• 2024

This study investigated the early life history of the Boleophthalmus pectinirostris living in the southern coastal Yeoja-man and compared the results with the same Gobiidae fishes. The brood stork used in the study were captured with bare hands in the tidal flats of Beolgyo-eup, Jeollanam-do, in June 2015. The amount of spawning was 411~11,688, and the eggs were short oval and the size was 1.40×0.72 mm. The time of hatched took 91 hours and 35 minutes at a water temperature of 25~27℃. Newly hatching larvae, the yolk sac had a total length of 3.02~3.31 (average 3.17±0.08, n=30) mm and did not eat rotifer. 4 days after hatching, the total length was 3.31~3.52 (3.43±0.07, n=30) mm, and as the mouth and anus opened, the fish transitioned to the preflexion larvae and fed. 14 days after hatching, the total length was 5.06~5.25 (5.16±0.06, n=30) mm, and the distal end of the vertebra was completely bent at 45° and the transitioned to the postflexion larvae. 41 days after hatching, the total length was 14.3~16.8 (15.4±0.85 mm, n=30), and the number of fins reached an integer of 5 first dorsal fins, 26~27 second dorsal fins, 24~27 anal fins, and 6 ventral fins, and the transitioned to the juveniles. As a result of the study, star-shaped melanophore were deposited from the front of the pectoral fin to the base of the caudal fin, which distinguished them in form from other postflexion larvae of Gobiidae fishes.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.