Wireless capsule endoscopy (WCE) offers a feasible noninvasive way to detect the whole gastrointestinal (GI) tract and revolutionizes the diagnosis technology. However, compared with wired endoscopies, the limited working time, the low frame rate, and the low image resolution limit the wider application. The progress of this new technology is reviewed in this paper, and the evolution tendencies are analyzed to be high image resolution, high frame rate, and long working time. Unfortunately, the power supply of capsule endoscope (CE) is the bottleneck. Wireless power transmission (WPT) is the promising solution to this problem, but is also the technical challenge. Active CE is another tendency and will be the next geneion of the WCE. Nevertheless, it will not come true shortly, unless the practical locomotion mechanism of the active CE in GI tract is achieved. The locomotion mechanism is the other technical challenge, besides the challenge of WPT. The progress about the WPT and the active capsule technology is reviewed.

Bleeding in the digestive tract is one of the most common gastrointestinal tract (GI) diseases, as well as the complication of some fatal diseases. Wireless capsule endoscopy (WCE) allows physicians to noninvasively examine the entire GI tract. However it is very laborious and time-consuming to inspect large numbers of WCE images, which limits the wider application of WCE. It is therefore important to develop an automatic and intelligent computer-aided bleeding detection technique. In this paper, a new method aimed at bleeding detection in WCE images is proposed. Colour texture features distinguishing the bleeding regions from non-bleeding regions are extracted in RGB and HSI colour spaces; then a neural network using the colour texture features as the feature vector inputs is designed to recognize the bleeding regions. The experiments demonstrate that the bleeding regions can be correctly recognized and clearly marked out. The sensitivity of the algorithm is 93% and the specificity is 96%.

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

This study presents an improved double closed‐loop current (fundamental and harmonics current) control method for inductive‐capacitive‐inductive (LCL) type shunt active power filter (SAPF), which is designed to enhance the robustness of the SAPF system to adapt weak grid application condition. Due to the variation of weak grid impedance, fundamental current control loop based on grid current feedback control method may become unstable, a robust parameters design method in discrete z ‐domain according to amplitude‐frequency and phase‐frequency characteristics is proposed to fit the grid impedance variation. The harmonics current of non‐linear load exists in a wide frequency range, normal resonant controller will cause the system unstable due to the negative −180° cross of phase at high‐frequency range, a digital phase‐lead resonant controller (PL‐RC) is proposed to extend the bandwidth of the harmonics current control, then the quantities of the PL‐RCs used to suppress harmonics can be increased considerably compared with normal resonant controller. Experimental results are presented to verify the effectiveness of the robust parameters design method and the proposed PL‐RC.