Denial constraints are vital in data quality management, but traditional mining algorithms struggle with time series data. To address this, we introduce a novel data quality rule, threshold Denial Constraints ($t$DCs), which enables predicate scaling in numerical contexts. We formalize the inference system for$t$DCs and demonstrate the monotonicity and abruptness of threshold predicates. To efficiently mine$t$DCs, we design the tDCDiscover algorithm, which leverages batch computation of differences and thresholds to significantly reduce the time required for acquiring homologous predicate evidence, achieving a 50% -66% decrease. Additionally, we introduce an evidence matrix to store evidence, lowering the complexity of evidence matching from$O(m)$to$O(1)$. We propose two pruning strategies: triviality pruning and prediction coverage pruning, to effectively decrease the search paths to one-fifth of their original number and eliminating at least 90% of unnecessary paths. We theoretically prove that tDCDiscover ensures minimal, valid, and complete results. Experimental results on eight real-world datasets demonstrate that, compared to the current state-of-the-art denial constraint mining techniques, tDCDiscover achieves more than double the efficiency when processing high-dimensional time series data. In downstream data cleaning tasks, tDCDiscover improves error detection precision by an average of 40% and repair accuracy by 18%, further offering advantages in time series data quality management.

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods.

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks.

Data dependency mining plays a crucial role in understanding data relationships. To address the increasing complexities of real-world data, Approximate Functional Dependencies (AFDs) have been introduced, building upon traditional FD. However, existing AFD approaches use static relaxation coefficients, limiting their effectiveness in capturing dependencies in noisy data. We propose a dynamic AFD variant, DAFD, which incorporates attribute error rates. We establish a bijection between DAFD and FD, develop its inference system, and introduce DAFDiscover, an algorithm for mining dependencies directly on noisy data. DAFDiscover matches the time and space complexity of SOTA AFD mining methods while offering superior performance. We theoretically prove its correctness, provide a method for calculating DAFD probabilities (DAFD- prob ), and derive a lower bound for DAFD's validity on dirty data. Experimental results on multiple public datasets demonstrate the semantic superiority of DAFD and the effectiveness of DAFDiscover compared to existing SOTA AFD mining techniques.

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%.

Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer's performance. The wearer need different assistance modes to reduce the metabolic rate when walking on different terrains. Therefore, assistance modes need to be selected according to different terrains for the wearer of soft Exosuit. Recently, our team has designed a stable terrain classification and recognition system (TCRS) for the soft Exosuit to discriminate terrains and estimating environmental features. Through this system, soft Exosuit can perceive the environment to auxiliary control of the locomotion modes. A depth sensor with an inertial measurement unit(IMU) can acquire to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from the origin 3D point cloud. Then, they are classified to estimate terrain environmental features, including the incline angle of the slope, the width, and the stairs' height. Finally, the TCRS was evaluated by classifying and recognizing five basic terrains in three different experimental scenarios while the subject was wearing the soft Exosuit with the TCRS module. The results show that the terrain classification accuracy rate reaches 97.74 %, and the environmental features estimation error is less than 15 %. The promising results indicate the robustness and the potential application of the presented TCRS to provide proper auxiliary force to reduce the metabolic rate of wearers on different terrains.