Sensor Intrusion Detection in Control Systems Using Estimation Theory

In this thesis, two different approaches to sensor intrusion detection are presented. In the first approach, an estimation algorithm using a bank of Kalman Filters is designed that is capable of estimating the intrusion signal when sensors are affected in control systems. The mathematical models of the control system will be estabilished and the system measurement will be shown and after that, various false signals, such as constant-type and ramp-type signal, will be selected as the intrusion signal to affect the system output mentioned above. The system measurement will be tested based on a bank of Kalman Filters. The probabilities of each intrusion state (affected and unaffected) of the control system will be calculated as a function of time. The estimation of the states from a bank of Kalman Filters together with the associated probabilities will determine whether the sensor is under attack or not by using the information from the estimation algorithm. The performance of the algorithm will be tested based on the various levels of the system and measurement noise.In the second approach, a new estimation algorithm is applied to detect the intrusion signal targeting the system mentioned above. By calculating the sample mean value of the system state and measurement in time, the changes of the system measurement can be detected by calculating the residual between the actual value and the theoretical sample mean value of the system measurement and in that case, the intrusion signal can be found. Thesis conclusions, summary and future work is also mentioned in the last chapter of this work

Advanced membrane-based electrode engineering toward efficient and durable water electrolysis and cost-effective seawater electrolysis in membrane electrolyzers

Researchers have been seeking for the most technically-economical water electrolysis technology for entering the next-stage of industrial amplification for large-scale green hydrogen production. Various membrane-based electrolyzers have been developed to improve electric-efficiency, reduce the use of precious metals, enhance stability, and possibly realize direct seawater electrolysis. While electrode engineering is the key to approaching these goals by bridging the gap between catalysts design and electrolyzers development, nevertheless, as an emerging field, has not yet been systematically analyzed. Herein, this review is organized to comprehensively discuss the recent progresses of electrode engineering that have been made toward advanced membrane-based electrolyzers. For the commercialized or near-commercialized membrane electrolyzer technologies, the electrode material design principles are interpreted and the interface engineering that have been put forward to improve catalytic sites utilization and reduce precious metal loading is summarized. Given the pressing issues of electrolyzer cost reduction and efficiency improvement, the electrode structure engineering toward applying precious metal free electrocatalysts is highlighted and sufficient accessible sites within the thick catalyst layers with rational electrode architectures and effective ions/mass transport interfaces are enabled. In addition, this review also discusses the innovative ways as proposed to break the barriers of current membrane electrolyzers, including the adjustments of electrode reaction environment, and the feasible cell-voltage-breakdown strategies for durable direct seawater electrolysis. Hopefully, this review may provide insightful information of membrane-based electrode engineering and inspire the future development of advanced membrane electrolyzer technologies for cost-effective green hydrogen production

Design and realization of precise indoor localization mechanism for Wi-Fi devices

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.Peer ReviewedPostprint (published version

Information-Preserved Blending Method for Forward-Looking Sonar Mosaicing in Non-Ideal System Configuration

Forward-Looking Sonar (FLS) has started to gain attention in the field of near-bottom close-range underwater inspection because of its high resolution and high framerate features. Although Automatic Target Recognition (ATR) algorithms have been applied tentatively for object-searching tasks, human supervision is still indispensable, especially when involving critical areas. A clear FLS mosaic containing all suspicious information is in demand to help experts deal with tremendous perception data. However, previous work only considered that FLS is working in an ideal system configuration, which assumes an appropriate sonar imaging setup and the availability of accurate positioning data. Without those promises, the intra-frame and inter-frame artifacts will appear and degrade the quality of the final mosaic by making the information of interest invisible. In this paper, we propose a novel blending method for FLS mosaicing which can preserve interested information. A Long-Short Time Sliding Window (LST-SW) is designed to rectify the local statistics of raw sonar images. The statistics are then utilized to construct a Global Variance Map (GVM). The GVM helps to emphasize the useful information contained in images in the blending phase by classifying the informative and featureless pixels, thereby enhancing the quality of final mosaic. The method is verified using data collected in the real environment. The results show that our method can preserve more details in FLS mosaics for human inspection purposes in practice

Evaluating the effect of curing conditions on the glass transition of the structural adhesive using conditional tabular generative adversarial networks

Owing to its structural advantages, adhesively bonding fibre-reinforced polymers have been a promising solution for strengthening constructions. However, the effectiveness of this technique is significantly influenced by the material properties of the adhesive layer, which are largely determined by its curing condition. A comprehensive analysis of the effect of curing conditions on structural adhesives is hampered by the lack of sufficient experimental data. To mitigate such a limitation, this present study utilises a deep machine learning (ML) tool, the conditional tabular generative adversarial networks (CTGAN), to generate plausible synthetic dataset for developing a robust data-driven model. An artificial neural network (ANN) was trained on synthetic data and tested on real data, following the "Train on Synthetic – Test on Real" philosophy. The ultimately developed CTGAN-ANN model was validated by newly conducted experiments and several published studies (R2 ≥ 0.95), which demonstrated the ability to provide accurate estimates of the glass transition temperature values of the polymer adhesive. A comprehensive evaluation of the effect of each curing condition variable on the adhesive was performed, which revealed the underlying relationships, indicating that curing temperature and curing time have a positive effect, but that curing humidity has a negative effect. The ML model developed could inform the practical use of the structural adhesive in civil engineering

Dietary Supplementation With Chinese Herbal Residues or Their Fermented Products Modifies the Colonic Microbiota, Bacterial Metabolites, and Expression of Genes Related to Colon Barrier Function in Weaned Piglets

To explore the feasibility of dietary Chinese herbal residue (CHR) supplementation in swine production with the objective of valorization, we examined the effects of dietary supplementation with CHR or fermented CHR products on the colonic ecosystem (i.e., microbiota composition, luminal bacterial metabolites, and expression of genes related to the intestinal barrier function in weaned piglets). We randomly assigned 120 piglets to one of four dietary treatment groups: a blank control group, CHR group (dose of supplement 4 kg/t), fermented CHR group (dose of supplement 4 kg/t), and a positive control group (supplemented with 0.04 kg/t virginiamycin, 0.2 kg/t colistin, and 3000 mg/kg zinc 0.04 kg/t virginiamycin, 0.2 kg/t colistin, and 3000 mg/kg zinc oxide). Our results indicate that dietary supplementation with CHR increased (P < 0.05) the mRNA level corresponding to E-cadherin compared with that observed in the other three groups, increased (P < 0.05) the mRNA level corresponding to zonula occludens-1, and decreased (P < 0.05) the quantity of Bifidobacterium spp. When compared with the blank control group. Dietary supplementation with fermented CHR decreased (P < 0.05) the concentration of indole when compared to the positive control group; increased (P < 0.05) the concentrations of short-chain fatty acids compared with the values measured in the CHR group, as well as the mRNA levels corresponding to interleukin 1 alpha, interleukin 2, and tumor necrosis factor alpha. However, supplementation with fermented CHR decreased (P < 0.05) interleukin 12 levels when compared with the blank control group. Collectively, these findings suggest that dietary supplementation with CHR or fermented CHR modifies the gut environment of weaned piglets

Multimodal ultrasound imaging: a method to improve the accuracy of sentinel lymph node diagnosis in breast cancer

AimThis study assessed the utility of multimodal ultrasound in enhancing the accuracy of breast cancer sentinel lymph node (SLN) assessment and compared it with single-modality ultrasound.MethodsPreoperative examinations, including two-dimensional ultrasound (2D US), intradermal contrast-enhanced ultrasound (CEUS), intravenous CEUS, shear-wave elastography (SWE), and surface localization, were conducted on 86 SLNs from breast cancer patients. The diagnostic performance of single and multimodal approaches for detecting metastatic SLNs was compared to postoperative pathological results.ResultsAmong the 86 SLNs, 29 were pathologically diagnosed as metastatic, and 57 as non-metastatic. Single-modality ultrasounds had AUC values of 0.826 (intradermal CEUS), 0.705 (intravenous CEUS), 0.678 (2D US), and 0.677 (SWE), respectively. Intradermal CEUS significantly outperformed the other methods (p<0.05), while the remaining three methods had no statistically significant differences (p>0.05). Multimodal ultrasound, combining intradermal CEUS, intravenous CEUS, 2D US, and SWE, achieved an AUC of 0.893, with 86.21% sensitivity and 84.21% specificity. The DeLong test confirmed that multimodal ultrasound was significantly better than the four single-modal ultrasound methods (p<0.05). Decision curve analysis and clinical impact curves demonstrated the superior performance of multimodal ultrasound in identifying high-risk SLN patients.ConclusionMultimodal ultrasound improves breast cancer SLN identification and diagnostic accuracy

Plasma metabolomic analysis reveals the therapeutic effects of Jiashen tablets on heart failure

BackgroundHeart failure is a chronic progressive condition that significantly affects the quality of life of patients with high hospitalization and mortality rates. Jiashen tablets (JST), a Chinese herbal formula, have been reported to be an effective treatment against heart failure, however the underlying mechanisms remain obscure. This study was designed to determine the effect of JST on the treatment of heart failure and delineate the underlying mechanisms by an untargeted metabolomics approach.Materials and methodsThe chronic heart failure model was established by the permanent ligation of the left anterior descending coronary artery in rats. The cardiac functions of rats, including left ventricular ejection fraction (LVEF) and fractional shortening (LVFS), left ventricular internal diameter end diastole (LVIDd) and end systole (LVIDs), and interventricular septum thickness in diastole (IVSd) and in systole (IVSs), were measured by echocardiography. Biochemical analysis and histopathological examination were also performed to evaluate therapeutic effects of JST for treating heart failure. UHPLC-QTOF-MS/MS coupled with multivariate statistical analyses were applied for plasma metabolic profiling to identify biomarkers and potential mechanisms of JST in the treatment of heart failure.ResultsJiashen tablets could improve the cardiac function of heart failure rats and thus ameliorate heart failure via enhancing rat LVEF and LVFS and decreasing LVIDd, LVIDs, IVSd, and IVSs. Results of biochemical analysis and histopathological examination revealed that JST could reduce the serum lactate dehydrogenase (LDH) activity and the level of NT-pro BNP, markers of heart failure and myocardial damage, and inhibit myocardial fibrosis. Furthermore, in metabolomics analysis, a total of 210 metabolites with significant differences were identified between heart failure rats and normal rats, among which 29 metabolites were significantly restored after JST treatment. These metabolites were primarily involved in tryptophan metabolism, branched-chain amino acid metabolism, fatty acids β-oxidation, and glycerophospholipid metabolism.ConclusionThe present study illustrated the therapeutic effect of JST for the treatment of heart failure and delineated the underlying mechanisms mainly relating to the regulation of amino acid metabolism and lipid metabolism in heart failure rats

Preparation of Broad-Spectrum Polyclonal Antibody and Development of an Indirect Competitive-Enzyme Linked Immunosorbent Assay for Multi-Residue Detection of Biphenyl Tetrazolium Sartans in Antihypertensive Health Foods

An indirect competitive-enzyme linked immunosorbent assay (ic-ELISA) was established to detect the multi-residue of biphenyl tetrazolium sartans in antihypertensive health foods. Candesartan was coupled with bovine serum albumin to obtain immunogen. New Zealand white rabbits were immunized and a broad-spectrum antibody was obtained by an antibody screening assay. The half maximal inhibitory concentrations (IC50) for candesartan, losartan carboxylic acid, losartan potassium, olmesartan, olmesartan medoxomil, irbesartan, valsartan and valsartan methyl ester were 0.2, 0.2, 0.7, 0.04, 0.6, 0.3, 0.9 and 2.4 ng/mL, respectively. The samples were extracted with methanol and the matrix effect was eliminated by diluting the extract with standard solutions. The average recoveries of the eight target compounds were in the range from 80.6% to 120.0% with coefficients of variation equal to or below 14.0%. The results of ic-ELISA were highly correlated with those of liquid chromatography-tandem mass spectrometry (LC-MS/MS) (r > 0.97), indicating high accuracy and good reliability of ic-ELISA