In the past several decades, there were presented different innovative technologies rather than traditional wind turbines for renewable energy that uses wind kinetic energy and remains in the air through aerodynamic forces. Unlike wind turbines with towers, their systems operate in a flight, and they are connected to a foundation by a cable that either transmits the energy generated at the airfoil or transmits mechanical energy to the ground. Nowadays, there are several existing and developing technologies; however, each of them has limitations and challenges. This work will present an analysis of air floating design for electricity generation at high altitudes. It is a tethered wind turbine with a Balloon system, which has a simple controlling system, relatively higher efficiency, and low-cost technology. The concept of the design is to model the electricity generation device powered by clean renewable energy, mainly wind power. Base on the concept of kite or helium balloon to provide enough buoyancy to keep the device working at certain altitude. To increase the energy conversion efficiency and the feasibility of the device, it is mostly used in the country, open area. Despite high efficiency which needs further investigation, the designed device is moveable, pollution free and little space consumed.

Gobang is one of the most ancient abstract strategy games for two players. The game is traditionally played on a board with black and white stones, where players take turns placing a colored stone on an empty intersection. The winner is the first player to form an unbroken chain of five stones, either horizontally, vertically, or diagonally. Although the rules of Gobang seem pretty straightforward, the game tree complexity is enormous since the board state is more intuitive than in other games. In this paper, we will implement an algorithm that will solve the Gobang game using artificial intelligence (AI) methods. The program will begin learning from scratch, then use self-play to produce training data, and eventually steadily build up its strength. The present work first focuses on the implementation of the supervised learning algorithm in the identification procedure in order to identify the position of the current fallen piece. This will be achieved by utilizing image processing and a convolutional neural network. Then a Gobang game procedure will be implemented using a game search algorithm, in which the state of the game is judged by means of a human-set function. After that, the function of judging the game state in the above game search algorithm will be changed to an artificial neural network (ANN) model, since it is convenient to train a model with a small dataset. Finally, the reinforcement learning algorithm will be applied to learn the artificial neural network model so that the playing level of the Gobang game program can be continuously improved.