• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.96-100
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• 2004

In the ship building industry various problems of erection is counterfeited due to formation of bottle necks in the block erection flow pattern This kind of problems cause accumulated problems in real-time erection right on the floor, When such a problem is approached, a support data of the entire erection sequence should be available, Here planning is done by reasoning about the future events in order to verify the existence of a reasonable series of actions to accomplish a goal. This technique helps in achieving benefits like handling search complications, in resolving goal conflicts and anticipation of bottleneck formation well in advance to take necessary countermeasures and boosts the decision support system, The data is being evaluated and an anticipatory function is to be developed This function is quite relevant in day to day planning operation. The system updates database with rearrangement of off-critical blocks in the erection sequence diagram, As a result of such a system, planners can foresee months ahead and can effectively make decisions regarding the control of loads on the man, machine and work flow pattern, culminating to an efficient load management. Such a foreseeing concept helps us in eliminating backtracking related adjustment which is less efficient compared to the look-ahead concept. An attempt is made to develop a computer program to update the database of block arrangement pattern based on heuristic formulation.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.303-314
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• 2023

Voice conversion, a technology that allows an individual's speech data to be regenerated with the acoustic properties(tone, cadence, gender) of another, has countless applications in education, communication, and entertainment. This paper proposes an approach based on the StarGAN-VC model that generates realistic-sounding speech without requiring parallel utterances. To overcome the constraints of the existing StarGAN-VC model that utilizes one-hot vectors of original and target speaker information, this paper extracts feature vectors of target speakers using a pre-trained version of Rawnet3. This results in a latent space where voice conversion can be performed without direct speaker-to-speaker mappings, enabling an any-to-any structure. In addition to the loss terms used in the original StarGAN-VC model, Wasserstein distance is used as a loss term to ensure that generated voice segments match the acoustic properties of the target voice. Two Time-Scale Update Rule (TTUR) is also used to facilitate stable training. Experimental results show that the proposed method outperforms previous methods, including the StarGAN-VC network on which it was based.