Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.Comment: 12 page

Distribution Grid Line Outage Detection with Privacy Data

Change point detection is important for many real-world applications. While sensor readings enable line outage identification, they bring privacy concerns by allowing an adversary to divulge sensitive information such as household occupancy and economic status. In this paper, to preserve privacy, we develop a decentralized randomizing scheme to ensure no direct exposure of each user's raw data. Brought by the randomizing scheme, the trade-off between privacy gain and degradation of change point detection performance is quantified via studying the differential privacy framework and the Kullback-Leibler divergence. Furthermore, we propose a novel statistic to mitigate the impact of randomness, making our detection procedure both privacy-preserving and have optimal performance. The results of comprehensive experiments show that our proposed framework can effectively find the outage with privacy guarantees.Comment: 5 page

Transaction-filtering data mining and a predictive model for intelligent data management

This thesis, first of all, proposes a new data mining paradigm (transaction-filtering association rule mining) addressing a time consumption issue caused by the repeated scans of original transaction databases in conventional associate rule mining algorithms. An in-memory transaction filter is designed to discard those infrequent items in the pruning steps. This filter is a data structure to be updated at the end of each iteration. The results based on an IBM benchmark show that an execution time reduction of 10% - 19% is achieved compared with the base case. Next, a data mining-based predictive model is then established contributing to intelligent data management within the context of Centre for Grid Computing. The capability of discovering unseen rules, patterns and correlations enables data mining techniques favourable in areas where massive amounts of data are generated. The past behaviours of two typical scenarios (network file systems and Data Grids) have been analyzed to build the model. The future popularity of files can be forecasted with an accuracy of 90% by deploying the above predictor based on the given real system traces. A further step towards intelligent policy design is achieved by analyzing the prediction results of files’ future popularity. The real system trace-based simulations have shown improvements of 2-4 times in terms of data response time in network file system scenario and 24% mean job time reduction in Data Grids compared with conventional cases.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Hyperconnectivity of the lateral amygdala in long-term methamphetamine abstainers negatively correlated with withdrawal duration

Introduction: Several studies have reported structural and functional abnormalities of the amygdala caused by methamphetamine addiction. However, it is unknown whether abnormalities in amygdala function persist in long-term methamphetamine abstainers.Methods: In this study, 38 long-term male methamphetamine abstainers (>12 months) and 40 demographically matched male healthy controls (HCs) were recruited. Considering the heterogeneous nature of the amygdala structure and function, we chose 4 amygdala subregions (i.e., left lateral, left medial, right lateral, and right medial) as regions of interest (ROI) and compared the ROI-based resting-state functional connectivity (FC) at the whole-brain voxel-wise between the two groups. We explored the relationship between the detected abnormal connectivity, methamphetamine use factors, and the duration of withdrawal using correlation analyses. We also examined the effect of methamphetamine use factors, months of withdrawal, and sociodemographic data on detected abnormal connectivity through multiple linear regressions.Results: Compared with HCs, long-term methamphetamine abstainers showed significant hyperconnectivity between the left lateral amygdala and a continuous area extending to the left inferior/middle occipital gyrus and left middle/superior temporal gyrus. Abnormal connections negatively correlated with methamphetamine withdrawal time (r = −0.85, p < 0.001). The linear regression model further demonstrated that the months of withdrawal could identify the abnormal connectivity (βadj = −0.86, 95%CI: −1.06 to −0.65, p < 0.001).Discussion: The use of methamphetamine can impair the neural sensory system, including the visual and auditory systems, but this abnormal connectivity can gradually recover after prolonged withdrawal of methamphetamine. From a neuroimaging perspective, our results suggest that withdrawal is an effective treatment for methamphetamine

Transaction-filtering data mining and a predictive model for intelligent data management

This thesis, first of all, proposes a new data mining paradigm (transaction-filtering association rule mining) addressing a time consumption issue caused by the repeated scans of original transaction databases in conventional associate rule mining algorithms. An in-memory transaction filter is designed to discard those infrequent items in the pruning steps. This filter is a data structure to be updated at the end of each iteration. The results based on an IBM benchmark show that an execution time reduction of 10% - 19% is achieved compared with the base case. Next, a data mining-based predictive model is then established contributing to intelligent data management within the context of Centre for Grid Computing. The capability of discovering unseen rules, patterns and correlations enables data mining techniques favourable in areas where massive amounts of data are generated. The past behaviours of two typical scenarios (network file systems and Data Grids) have been analyzed to build the model. The future popularity of files can be forecasted with an accuracy of 90% by deploying the above predictor based on the given real system traces. A further step towards intelligent policy design is achieved by analyzing the prediction results of files’ future popularity. The real system trace-based simulations have shown improvements of 2-4 times in terms of data response time in network file system scenario and 24% mean job time reduction in Data Grids compared with conventional cases

Path-Source Oriented Session Identification Based on Linked Referrers and Log Indexing

Summary Web usage mining has been widely adopted in various fields such as optimizing site structure, user-behavior analysis, personalized web services and system performance tuning. Although much research has been done against web log mining algorithms and log preprocessing techniques, the study of efficient retrieval of the structured contents for web log mining is seldom reported. In this paper, we first show that people are much more interested in discovering user navigation based on various path-sources. Then, we present a novel session identification algorithm Referrer Link based on discovering linked referrers to serve source-oriented path mining. Next, an efficient web log indexing and path extracting technique is introduced to provide structured web log data for general purpose log mining. The experimental results has shown that the accuracy of the mining results conducted against the sessions discovered by the proposed Referrer Link algorithm is 10% higher in average compared with Time-out approach

Dynamic and scalable storage management architecture for Grid Oriented Storage devices

Most of currently deployed Grid systems employ hierarchical or centralized approaches to simplify system management. However, the approaches cannot satisfy the requirements of complex Grid applications which involve hundreds or thousands of geographically distributed nodes. This paper proposes a Dynamic and ScalableStorageManagement (DSSM) architecture for GridOrientedStorage (GOS) devices. Since large-scale data intensive applications frequently involve a high degree of data access locality, the DSSM divides GOS nodes into multiple geographically distributed domains to facilitate the locality and simplify the intra-domain storagemanagement. Dynamic GOS agents selected from the domains are organized as a virtual agent domain in a Peer-to-Peer (P2P) manner to coordinate multiple domains. As only the domain agents participate in the inter-domain communication, system wide information dissemination can be done far more efficiently than flat flooding. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. The decentralized architecture of DSSM avoids the hierarchical or centralized approaches of traditional Gridarchitectures, eliminates large-scale flat flooding of unstructured P2P systems, and provides an interoperable, seamless, and infinite storage pool in a Grid environment. The DSSM architecture is validated by a proof-of-concept prototype system.Peer reviewe

Prevalence and influencing factors of suicide in first-episode and drug-naive young major depressive disorder patients with impaired fasting glucose: a cross-sectional study

BackgroundAn association exists between major depression disorder (MDD), suicide attempts, and glucose metabolism, but suicide attempts in young MDD patients with comorbid impaired fasting glucose (IFG) have been less well studied. The purpose of this study was to examine the prevalence and risk factors for suicide attempts in young, first-episode, drug-naive (FEDN) MDD patients with comorbid IFG. MethodsWe recruited 917 young patients with FEDN MDD, 116 of whom were judged to have combined IFG because their blood glucose was >6.0. We collected anthropological and clinical data on all of them. The Hamilton Depression Scale (HAMD) score, the Hamilton Anxiety Scale (HAMA) score and the Positive and Negative Syndrome Scale (PANSS) positive subscale score were used to assess their clinical symptoms. Blood glucose, plasma thyroid function and lipid indicators were measured. ResultsThe prevalence of suicide attempts in young MDD patients with IFG was 32.8% (38/116). Furthermore, among young MDD patients with comorbid IFG, suicide attempters had more severe depression and anxiety symptoms, more comorbid psychotic symptom, higher levels of antibody of thyroid stimulating hormone and thyroid peroxidases (TPOAb), and more severe lipid metabolism disorders than those without suicide attempts. In addition, HAMA scores and TPOAb were independently associated with suicide attempts in young patients with FEDN MDD. ConclusionOur study suggests that young MDD patients with IFG have a high rate of suicide attempts. Some clinical symptoms and thyroid function parameters may be the risk factor for suicide attempts in young MDD patients with impaired glucose metabolism