Raspberry Pi based lap counter for amateur car racing
This article presents the design of a lap counter and timer that can be used in amateur car racing. Main functions of this computer to count laps, measure lap time, vehicle speed and accelerations. The system uses a GPS module, accelerometer and magnetometer sensors.
A novel movies recommendation algorithm based on reinforcement learning with DDPG policy
English original movies played an important role in English learning and communication. In order to find the required movies for us from a large number of English original movies and reviews, this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies. In fact, although the conventional movies recommendation algorithms have solved the problem of information overload, they still have their limitations in the case of cold start-up and sparse data.,To solve the aforementioned problems of conventional movies recommendation algorithms, this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning, which uses the deep deterministic policy gradient (DDPG) algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one. Meanwhile, a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.,In order to verify the feasibility and validity of the proposed algorithm, the state of the art and the proposed algorithm are compared in indexes of RMSE, recall rate and accuracy based on the MovieLens English original movie data set for the experiments. Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.,Applying the proposed algorithm to recommend English original movies, DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.
GPS attitude determination using a genetic algorithm
In this paper, a new technique that uses a specially tailored genetic algorithm is proposed for attitude determination via GPS carrier phase observables. The technique overcomes restrictions due to computational overheads incurred by existing techniques such as the ambiguity function method. We present experimental results which show that the algorithm is able to efficiently search the complex search space imposed by the problem in addition to being immune to cycle slips compared to other conventional methods.