• Journal of Veterinary Science
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• v.22 no.3
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• pp.34.1-34.7
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• 2021

Anti-lipopolysaccharide (LPS) antibody administration has the potential benefits of neutralizing and consequently controlling rumen-derived LPS during subacute ruminal acidosis. Four Holstein bulls were used in this crossover study with a 2-week wash-out period. Anti-LPS antibody (0 or 4 g) was administered once daily for 14 days. Significantly lower ruminal LPS and higher 1-h mean ruminal pH were identified in the 4 g group. However, blood metabolites, acute-phase proteins, cytokines, and hepatic transcriptomes were not different between the two groups. Therefore, anti-LPS antibody administration mitigated ruminal LPS release and pH depression without accompanying responses in acute-phase inflammation or hepatic transcriptomic expression.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.406-417
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• 2014

This paper presents a numerical optimization method to improve the performance of the propeller with Turbo-Ring using real-coded genetic algorithm. In the presented method, Unimodal Normal Distribution Crossover (UNDX) and Minimal Generation Gap (MGG) model are used as crossover operator and generation-alternation model, respectively. Propeller characteristics are evaluated by a simple surface panel method "SQCM" in the optimization process. Blade sections of the original Turbo-Ring and propeller are replaced by the NACA66 a = 0.8 section. However, original chord, skew, rake and maximum blade thickness distributions in the radial direction are unchanged. Pitch and maximum camber distributions in the radial direction are selected as the design variables. Optimization is conducted to maximize the efficiency of the propeller with Turbo-Ring. The experimental result shows that the efficiency of the optimized propeller with Turbo-Ring is higher than that of the original propeller with Turbo-Ring.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.238-241
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• 2002

With the popularity of utilization of the Internet among people, the amount of data in the network rapidly increased. So that, the fall of response time from WWW server, which is caused by the network traffic and the burden on m server, has become more of an issue. This problem is encouraged the rearch by redundancy of requesting the same pages by many people, even though they browse the same the ones. To reduce these redundancy, WWW cache server is used commonly in order to store m page data and reuse them. However, the technical uses of WWW cache that different from CPU and Disk cache, is known for its difficulty of improving the cache hit rate. Consecuently, it is difficult to choose effective WWW data to be stored from all data flowing through the WWW cache server. On the other hand, there are room for improvement in commonly used cache replacement algorithms by WWW cache server. In our study, we try to realize a WWW cache server that stresses on the improvement of the stresses of response time. To this end, we propose the new cache replacement algorithm by focusing on the utilizable information of network distance from the WWW cache server to WWW server that possessing the page data of the user requesting.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• Nuclear Engineering and Technology
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• v.41 no.8
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• pp.989-994
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• 2009

We at Mitsubishi have lined up Gen-III+ solutions for a wide variety of global customers: ATMEA1 of the 1100MWe class, and an APWR with the largest capacity of 1700MWe. In this paper, we would like to introduce the APWR. With an increased requirement for nuclear power generation as an effective countermeasure against global warming, we have established the APWR plant, a large-capacity Mitsubishi standard reactor combining our accumulated experience and technology as an integrated PWR plant supplier. The APWR plant has achieved high reliability, safety and enhanced economy based on a technology that has been developed with the support of the government and utilities through improvement and standardization programs of light water reactors. Currently, Tsuruga Units 3 and 4, the first two APWRs, are undergoing licensing, while we are making efforts to obtain the standard design certification (DC) of US-APWR and preparing for the European Utility Requirements (EUR) compliance assessment of EU-APWR. Mitsubishi Heavy Industries, Ltd. (MHI) positions the APWR as a core technology that will contribute to the prevention of global warming and meet worldwide requirements.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• Structural Engineering and Mechanics
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• v.9 no.3
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• pp.289-304
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• 2000

This study discusses on the experimental and analytical results of the global buckling tests, carried out on aluminum alloy double layer space grids composed of tubular members, ball joints and connecting bolts at the member ends, with the purpose of demonstrating the effectiveness of a simplified analysis method using an equivalent slenderness ratio for the members. Because very few experiments have been carried out on this type of aluminum space grids, the buckling behavior is investigated experimentally over the post buckling regions using several space grid specimen with various values for the member slenderness ratio. The observed behavior duping the experiments is compared with the analytically obtained results. The comparison is made based on two different schemes; one on the plastic hinge method considering a bending moment-axial force interaction for members and the other on a method using an equivalent slenderness ratio. It is confirmed that the equivalent slenderness method can be effectively applied, even in the post buckling regions, once the effects of the rotational rigidity at the ball joints are appropriately evaluated, because the rigidity controls the buckling behavior. The effectiveness of the equivalent slenderness method will be widely utilized for estimation of the ultimate strength, even in post buckling regions for large span aluminum space grids composed of an extreme large number of nodes and members.

• Steel and Composite Structures
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• v.8 no.6
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• pp.511-530
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• 2008

This paper discusses a feasibility of a new type of two-way system for single layer lattice domes with nodal eccentricity by investigating the dynamic behavior under earthquake motions. The proposed dome is composed of two main arches, intersecting each other with T-joint struts to provide space for tensioning membranes. The main purposes of this study are to calculate the nonlinear dynamic response under severe earthquake motions and to see the possibility of using this new type of two-way system for single layer lattice domes against earthquake motions. The results show that the main arches remain elastic except yielding of the joints of strut members that can be used to absorb some amount of strain energy at strong earthquake motion. Consequently, deformation of the main arches can be reduced and any heavy damages on the main arches can be minimized. A kind of damage-control characteristic appeared in this system may be utilized against severe earthquake motions, showing a possibility of designing a new type of single layer lattice dome.

• Journal of Sensor Science and Technology
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• v.7 no.1
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• pp.17-22
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• 1998

A HARP (high-gain avalanche rushing amorphous photoconductor) image pickup tube using $4{\mu}m$ thick a-Se photoconductive film was fabricated and its characteristics were investigated. When the target voltage was increased more than 360 V, the signal current increases rapidly but the dark current of the tube was suppressed less than 3.2 nA up to the voltage of 490 V And the quantum efficiency of the target was about 4.3 at the electric field of $1.1{\times}10^{6}V/cm$ and the wavelength of 440 nm. Also the amplitude response of the HARP tube was 7.5% at 800 TV lines, and the decay lag was 3.4%.