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Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
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INTRODUCTION: Obturator hernia is an extremely rare type of hernia with relatively high mortality and morbidity. Its early diagnosis is challenging since the signs and symptoms are non specific. PRESENTATION OF CASE: Here in we present a case of 70 years old women who presented with complaints of intermittent colicky abdominal pain and vomiting. Plain radiograph of abdomen showed acute dilatation of stomach. Ultrasonography showed small bowel obstruction at the mid ileal level with evidence of coiled loops of ileum in pelvis. On exploration, Right Obstructed Obturator hernia was found. The obstructed Intestine was reduced and resected and the obturator foramen was closed with simple sutures. Postoperative period was uneventful. DISCUSSION: Obturator hernia is a rare pelvic hernia and poses a diagnostic challenge. Obturator hernia occurs when there is protrusion of intra-abdominal contents through the obturator foramen in the pelvis. The signs and symptoms are non specific and generally the diagnosis is made during exploration for the intestinal obstruction, one of the four cardinal features. Others are pain on the medial aspect of thigh called as Howship Rombergs sign, repeated attacks of Intestinal Obstruction and palpable mass on the medial aspect of thigh. CONCLUSION: Obturator hernia is a rare but significant cause of intestinal obstruction especially in emaciated elderly woman and a diagnostic challenge for the Doctors. CT scan is valuable to establish preoperative diagnosis. Surgery either open or laproscopic, is the only treatment. The need for the awareness is stressed and CT scan can be helpful.
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In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for "explaining" the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.