Rebranding Announcement: We Are Setting IT Unicorn!

SettingItUnicorn:从Showbox到区块链游戏
SettingItUnicorn(前身为ShowboxGroup)宣布与新加坡品牌公司LINKWE合并,并收购了区块链咨询公司SATORIBCCPTE.LTD.的全部股份。此次变革不仅包括品牌更名,还涉及视觉形象重塑及产品线整合,旨在提供包含区块链元素的创新游戏体验。

When we started Showbox five years ago, none of us could have ever predicted how far we would come; and the moments and success stories we have behind us. And, yet again, here we are today—under the new name SETTING IT UNICORN.

In September 2018, we, SETTING IT GROUP (former name: SHOWBOX GROUP), merged a Singapore brand Company LINKWE and acquired 100% stake in a Singapore blockchain consulting company SATORI BCC PTE. LTD., to expand our business. So, we decided it was time for a change and that, with moving to our new office space, we might do a bit more than remodeling. That’s how we came up with the idea to create a new visual identity and unite all products under a one brand – SETTING IT UNICORN.

A number of our customers have already experienced and become familiar with SHOWBOX. Thank you for providing the feedback and helping us every step of the way through the APP development. Now, we are pouring our hearts and souls into the brand new version release. We are dedicated to providing more rewarding and exciting games, not only the traditional ones but related to blockchain, which will reflect our forward thinking changes.

Please let us know what you think. We’re pretty happy with the new change and hope you are too. Of course, and as always, if you have any questions or comments, or just want to chat, let us know. We’ll keep you updated as we get closer to the new APP launch.

Thank you.

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比路,拓展到其他智能优化算法的研究与改进中。
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