With increasing wind power penetration, the inherent uncertainty of wind power poses significant challenges to dispatch decisions in power systems. To address this issue, this paper proposes a two-stage distributionally robust chance-constrained (TDRC) model for the unit commitment problem with wind power uncertainty. In this model, an ambiguity confidence set is developed to characterise wind power uncertainty with unknown probability distributions, and wind power curtailment and load shedding levels are modelled as chance constraints to balance wind power uncertainty and system security of dispatch decisions. A hybrid parallel solution (HPS) is proposed for efficient computation by integrating Benders decomposition (BD) and column-and-constraint generation (C&CG) methods. Case studies on the IEEE 24- and 118-bus systems demonstrate the rationality of the proposed approach, while experiments on a practical 126-bus system using the cyber-physical power system (CPPS) dispatch platform further validate the effectiveness and practical applicability of the proposed TDRC model.

False data injection (FDI) attacks can mislead the system operator to conduct incorrect dispatch decisions, causing cyber-induced physical line overloads. However, traditional false data is constructed either further from normal data and easy to detect, or not effective to overload multiple lines. To improve both attack stealth and overload impacts, this paper proposes a bilevel cyber-induced overloads (CIO) mechanism that can cause a predefined number of multi-line overloads considering post-attack economic dispatch, where the injected false data is minimised to improve the attack stealth. Within this mechanism, a detailed CIO attack model is formulated that explicitly incorporates the post-attack economic dispatch, enabling it to design more practical attack strategies. One advanced feature of this CIO attack model is the optimal selection of overloaded lines for the bilevel optimization of cyberattack resources and post-attack impacts in terms of line overloads, operation costs, and load loss. To solve the proposed model, it is converted into a single-level nonlinear model by a strong duality theory, and then CIO attacks are discretised to convert this model into a mixed-integer linear programming (MILP) problem. Case studies conducted on an IEEE 14-bus power system and an industrial 126-bus equivalent power system validate the superiority and effectiveness of our proposed approach.

Line hardening is regarded as a critical defense measure against malicious attacks in power systems. However, this defense strategy often ignores the uncertainty and multistage nature of malicious attacks. Moreover, with high wind power penetration in power system operation, the inherent volatility of wind power may compromise the performance of the defense strategy against malicious attacks. To address such issues, this article proposes an adaptive robust planner-attacker-operator (AR-PAO) model that accounts for both volatile wind power and multistage uncertain malicious attacks within a line hardening framework. A novel solution algorithm is then developed to solve the AR-PAO model for the optimal defense strategy, in which it improves computational efficiency. Comparative simulation results on two IEEE Reliability Test Systems validate the effectiveness of the proposed approach.

This letter develops a high-risk regional load attack mechanism in smart grids with incomplete network information. Different from previous research, this attack mechanism enables an attacker to launch a regional load attack with network information about an arbitrary attack region, while minimising the deviation in corrupted data to enhance the stealth of this attack. Such an attack only corrupts a limited number of loads while still overloading multiple lines within a targeted attack region, resulting in significant impacts on smart grids. Case studies conducted on two modified IEEE test systems validate the effectiveness of our proposed attack mechanism and pave the foundation for the future development of practical defensive strategies.

This letter proposes an island-maximising attack mechanism to maximise the number of power islands in cyber-physical power systems (CPPS) by disrupting lines. Specifically, a single-level mixed-integer programming (MIP) model is developed with a graph-theoretic approach, which is able to divide CPPS into power islands, while intra-island line overloads are induced in a high-stealth manner with increasing load shedding and economic loss. Case studies conducted on modified IEEE 14-bus and practical 36-zone Great Britain systems validate the effectiveness of our proposed island-maximising approach.

Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of interest on the gigapixel of whole slide images (WSIs), which however is laborious and often biased. In this paper, we propose a weakly supervised framework based on Tokens-to-Token Vision Transformer (WS-T2T-ViT) for accurate NPC classification with only a slide-level label. The label of tile images is inherited from their slide-level label. Specifically, WS-T2T-ViT is composed of the multi-resolution pyramid, T2T-ViT and multi-scale attention module. The multi-resolution pyramid is designed for imitating the coarse-to-fine process of manual pathological analysis to learn features from different magnification levels. The T2T module captures the local and global features to overcome the lack of global information. The multi-scale attention module improves classification performance by weighting the contributions of different granularity levels. Extensive experiments are performed on the 802-patient NPC and CAMELYON16 dataset. WS-T2T-ViT achieves an area under the receiver operating characteristic curve (AUC) of 0.989 for NPC classification on the NPC dataset. The experiment results of CAMELYON16 dataset demonstrate the robustness and generalizability of WS-T2T-ViT in WSI-level classification.

Musculoskeletal models play an essential role in ankle rehabilitation research. The majority of the existing models have established the relationship between EMG and joint torque. However, EMG signal acquisition requires higher clinical conditions, such as sensitivity to external circumstances, motion artifacts and electrode position. To solve the nonlinear and time-varying nature of joint movement, a Functional Electrical Stimulation (FES) model was proposed in this study to simulate the whole process of ankle dorsiflexion. The model is combined with muscle contraction dynamics based on Hill model and ankle inverse dynamics to connect FES parameters, torques, and ankle angles. In addition, the extended Kalman filter (EKF) algorithm was applied to identify the unknown parameters of the model. Model validation experiment was performed by acquiring the actual data of healthy volunteers. Results showed that the root mean square error (RMSE) and normalized root mean square error (NRMSE) of this model were 11.93%±0.53% and 1.39°±0.26°, respectively, which means it can effectively predict the output variation of ankle joint angle while changing electrical stimulation parameters. Therefore, the proposed mode is essential for developing closed-loop feedback control of electrical stimulation and has the potential to help patients to conduct rehabilitation training.

Biomedical image segmentation and classification are critical components in a computer-aided diagnosis system. However, various deep convolutional neural networks are trained by a single task, ignoring the potential contribution of mutually performing multiple tasks. In this paper, we propose a cascaded unsupervised-based strategy to boost the supervised CNN framework for automated white blood cell (WBC) and skin lesion segmentation and classification, called CUSS-Net. Our proposed CUSS-Net consists of an unsupervised-based strategy (US) module, an enhanced segmentation network named E-SegNet, and a mask-guided classification network called MG-ClsNet. On the one hand, the proposed US module produces coarse masks that provide a prior localization map for the proposed E-SegNet to enhance it in locating and segmenting a target object accurately. On the other hand, the enhanced coarse masks predicted by the proposed E-SegNet are then fed into the proposed MG-ClsNet for accurate classification. Moreover, a novel cascaded dense inception module is presented to capture more high-level information. Meanwhile, we adopt a hybrid loss by combining a dice loss and a cross-entropy loss to alleviate the imbalance training problem. We evaluate our proposed CUSS-Net on three public medical image datasets. Experiments show that our proposed CUSS-Net outperforms representative state-of-the-art approaches.

Functional electrical stimulation (FES) provides an effective way for foot drop (FD) correction. To overcome the redundant and blind stimulation problems in the state-of-the-art methods, this study proposes a closed-loop scheme for an adaptive electromyography (EMG)-modulated stimulation profile. The developed method detects real-time angular velocity during walking. It provides feedbacks to a long short-term memory (LSTM) neural network for predicting synchronous tibialis anterior (TA) EMG. Based on the prediction, it modulates the stimulation intensity, taking into account of the subject-specific dead zone and saturation of the electrically evoked activation. The proposed method is tested on ten able-bodied participants and six FD subjects as proof of concept. The experimental results show that the proposed method can successfully induce the dorsiflexion of the ankle joint, and generate an activation pattern similar to a natural gait, with the mean Correlation Coefficient of 0.9021. Thus, the proposed method has the potential to help patients to retrieve normal gait.

Polysomnography (PSG) screening for obstructive sleep apnea (OSA) is time consuming. The OSA classification is very important for medical scientists and machine learning researchers. In the current work, we developed a classification method for electrocardiogram (ECG) data. The data set has two labels: sleep disorders or not. As a result, Active Learning is used as a classification technique. Data stream classification in a non-stationary environment is attaining more attention recently. It is a highly challenging task, since the concept drift and limited labeled data. Therefore, a classification model is needed to be struggling with concept drift detection and the need of labeled data. To solve these issues, we propose an efficient semi-supervised method in this paper which uses Active Learning to detect concept drift in an unsupervised way and Classifiers Ensemble to keep higher predictions combined with weighted majority voting. Experiments results on real-world and synthetic data-sets show the effectiveness of the proposed approach. For the initial experiment, we use an existing data set. The data set includes data for every 10 seconds, up to 6000 seconds, and 35 patients. We have used 80% of the data for training purposes and 20% of the data for testing purposes. Active Learning results show that our method can effectively detect OSA. The accuracy of the predicted result is 71%. Future research in this area will be to obtain data from hospitals and use our developed algorithms for OSA classification and prediction.

Intrabody communication (IBC) is an effective way to connect various kinds of wearable devices attached on or under the surface of the body, but it is important to quantitatively evaluate the biological effects of the IBC signal on the human body before its further application. The research described in this paper analyzed the responses of HSF (human skin fibroblast) cells exposed to IBC electrical signals. A galvanic coupling IBC signal transmitting system was designed to expose the experimental samples with different amplitudes (from 0 V to 6 V or 0 mA to 4 mA), different frequencies (from 10 kHz to 1 MHz), and different duration times (12 h and 24 h). The control groups were unexcited. Cell morphology and activity were evaluated with inverted microscope and MTT assays. The cell survival rates of all the experiment groups were in the range of 90% to 110%. Then, the data was analyzed by t -tests to assess whether there were statistically significant differences. The results showed that p values were greater than 0.05, so there were no significant differences between the experimental and control groups. Therefore, it can be concluded that the IBC signals do not have a significant effect on HSF cells.

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

The human machine interface (HMI) is a main communication method between human and computer. Through current HMI, a machine receives and accurately responds to the commands instructed by the users. In the next generation of HMI, machines will be required to deal with more challenging problems/decisions (such as affective evaluations, ethical quandaries, and other innovations) in a self-governing manner. Thus, future HMI should be able to provide information about users' emotion to the machine for affective evaluation. In this paper, we focus on the natural connection method that can improve machines in making the acquaintance of the users. However, connecting sensors scattered on the human body poses serious problems concerning comfort and convenience. Therefore, the authors introduce the Intra Body Communication (IBC) for connecting various physiological sensors on the human body such that the physiological information can enrich the capability of the computer in cognition of the user's emotion. In addition, the authors also reported two pilot studies: using the IBC for connecting the physiological sensor on the human body and using the physiological parameters to estimate the degree of fatigue of the user.

In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.

In recent years, the increasing number of wearable devices on human has been witnessed as a trend. These devices can serve for many purposes: personal entertainment, communication, emergency mission, health care supervision, delivery, etc. Sharing information among the devices scattered across the human body requires a body area network (BAN) and body sensor network (BSN). However, implementation of the BAN/BSN with the conventional wireless technologies cannot give optimal result. It is mainly because the high requirements of light weight, miniature, energy efficiency, security, and less electromagnetic interference greatly limit the resources available for the communication modules. The newly developed intra-body communication (IBC) can alleviate most of the mentioned problems. This technique, which employs the human body as a communication channel, could be an innovative networking method for sensors and devices on the human body. In order to encourage the research and development of the IBC, the authors are favorable to lay a better and more formal theoretical foundation on IBC. They propose a multilayer mathematical model using volume conductor theory for galvanic coupling IBC on a human limb with consideration on the inhomogeneous properties of human tissue. By introducing and checking with quasi-static approximation criteria, Maxwell's equations are decoupled and capacitance effect is included to the governing equation for further improvement. Finally, the accuracy and potential of the model are examined from both in vitro and in vivo experimental results.