Short-Term Hydro Generation Scheduling Of The Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm

An improved binary-coded whale optimization algorithm (IBWOA) is proposed to solve the complex nonlinear problem of short-term hydropower generation scheduling (STHGS). The spatial optimal load distribution is combined with the temporal unit commitment combination model, and the binary array is used to represent the start/stop state of the unit. Sigmoid Function (SF) is used to solve the correspondence between binary array and real number. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. The unit commitment (UC) subproblem was solved by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint, and the economic load scheduling (ELD) subproblem was solved by an optimal stable load distribution table (OSLDT). The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. The proposal is applied to solve the STHGS of the Three Gorges hydropower station. The results show that the method has good convergence, stability, fast calculation speed, and high optimization accuracy.

A Low-cost Artificial Neural Network Model for Raspberry Pi

In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.

A Polarization Reconfigurable Aperture-Coupled Microstrip Antenna and Its Binary Array for MIMO

In the paper, a singly fed circular patch antenna with polarization diversity is proposed, and its binary array for MIMO application is explored as well. The air substrate and aperture-coupled feed structure are adopted to increase bandwidth and simplify the bias circuit of PIN diodes. By controlling the states of four PIN diodes on the patch, the proposed antenna can produce linear polarization (LP), left- or right-hand circular polarization (LHCP or RHCP). For each polarization sense, the antenna exhibits wide impedance bandwidth, high gain and low cross-polarization. Two antennas are orthogonally placed to form a binary array for MIMO application, which has high isolation and low envelope correlation. The antenna and its array have advantages of simple biasing network, easy fabrication and adjustment, which can be widely applied in wireless communication systems.