Bidirectional Hetero-associative Memory Research Articles

Flexible Ring Sensor Array and Machine Learning Model for the Early Blood Leakage Detection during Dialysis

Severe blood leakage resulting from the detachment of dialysis tubing is often difficult to detect by nurses in busy clinics. This paper presents a flexible blood leakage detection system featuring a ring-light sensor array with an operating wavelength of 500–700 nm, which is held in place by the gauze covering the dialysis puncture site. A ring-light sensor is connected to a bidirectional hetero-associative memory network, which interprets detected changes in signal strength, the output signal of which is transmitted via WiFi to a server at the nursing station where a machine learning algorithm determines whether blood leakage has occurred. The compact design of this early warning system greatly enhances the comfort and mobility of patients undergoing dialysis. The efficacy of the proposed system was demonstrated in experiments involving artificial blood.

Inflow and outflow stenoses screening on biophysical experimental arteriovenous graft using big spectral data and bidirectional associative memory machine learning model

Long-term repeating traumatic puncture is required for dialysis therapy, which results in frequent thrombosis and graduate vascular access stenosis, such as inflow or outflow stenosis and coexistence of both. An arteriovenous graft has a higher patency rate than an arteriovenous fistula. This study intends to use the dual-channel auscultation-based non-invasive method to screen inflow and outflow stenoses. Frequency analysis is used to decompose phonoangiography (PAG) signals to frequency features using the different data length of acoustic data. Burg autoregressive method is employed to extract the key frequency parameters from sufficient spectral data, including characteristic frequencies and distinct peaks of power spectral densities (PSDs). In big data processing, PSDs and the degree of stenosis (DOS) have been validated to show a positive correlation with sufficient big spectral data. An intelligent machine learning model, bidirectional hetero-associative memory network (BHAMN), is carried out to identify the level of DOS at the inflow site, the mid-site, or the outflow site of a vascular access. The experimental results will indicate that the proposed intelligent machine learning model has higher hit rates.

Multiple Fault Location in a Photovoltaic Array Using Bidirectional Hetero-Associative Memory Network in Micro-Distribution Systems

In manual maintenance inspections of large-scaled photovoltaic (PV) or rooftop PV systems, several days are required to survey the entire PV field. To improve reliability and shorten the amount of time involved, this study proposes an electrical examination-based method for locating multiple faults in the PV array. The maximum power point tracking (MPPT) algorithm is used to estimate the maximum power of each PV panel; this is then compared with metering the output power of PV array. Power degradation indexes as input variables are parameterized to quantify the degradation between estimated maximum PV output power and metered PV output power, which can be categorized into normal condition, grounded faults, open-circuit faults, bridged faults, and mismatch faults. Bidirectional hetero-associative memory (BHAM) networks are then used to associate the inputs and locate multiple faults as output variables within the PV array. For a rooftop PV system with two strings, experimental results demonstrate that the proposed model has computational efficiency in learning and detection accuracies for real-time applications, and that its algorithm is easily implemented in a mobile intelligent vehicle.

Bidirectional Hetero-Associative Memory Network With Flexible Sensors and Cloud Computing for Blood Leakage Detection in Intravenous and Dialysis Therapy

Infiltration and blood leakage are serious life-threatening complications during intravenous administration and dialysis therapy. Serious infiltrations, such as drips and drugs, may cause inflection, sepsis, and extent of tissue damage. Blood leakage and blood loss are serious complications, as it takes only a few minutes to lose over 40% of adult blood volume, potentially resulting in mortality. Therefore, we propose integrating the flexible sensors and bidirectional hetero-associative memory (BHAM) network into a cloud computing-based warning tool for the detection of infiltration and blood leakage. The flexible sensors are arranged in two array patterns for liquid leakage detection and are fabricated on a soft substrate via a screen-printing technique. The BHAM network constructs a virtual alarm unit in an embedded system or tablet PC. This early warning unit can be employed to identify risk levels via a wireless network and cloud computing, and then indicates warning information on a remote monitor device or a mobile device. The proposed detection model has been validated experimentally, and the experimental results demonstrate its feasibility.

Depth of treatment sensitive noise resistant dynamic artificial neural networks model of recall in people with prosopagnosia

The Fusiform Face Area (FFA) is the brain region considered to be responsible for face recognition. Prosopagnosia is a brain disorder causing the inability to a recognise faces that is said to mainly affect the FFA. We put forward a model that simulates the capacity to retrieve label associated with faces and objects depending on the depth of treatment of the information. Akin to prosopagnosia, various localised "lesions" were inserted into the network in order to evaluate the degradation of performance. The network is first composed of a Feature Extracting Bidirectional Associative Memory (FEBAM-SOM) to represent the topological maps allowing the categorisation of all faces. The second component of the network is a Bidirectional Heteroassociative Memory (BHM) that links those representations to their semantic label. For the latter, specific semantic labels were used as well as more general ones. The inputs were images representing faces and various objects. Just like in the visual perceptual system, the images were pre-processed using a low-pass filter. Results showed that the network is able to associate the extracted map with the correct label information. The network is able to generalise and is robust to noise. Moreover, results showed that the recall performance of names associated with faces decrease with the size of lesion without affecting the performance of the objects. Finally, results obtained with the network are also consistent with human ones in that higher level, more general labels are more robust to lesion compared to low level, specific labels.

Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory

Brain‐inspired, artificial neural network approach offers the ability to develop attractors for each pattern if feedback connections are allowed. It also exhibits great stability and adaptability with regards to noise and pattern degradation and can perform generalization tasks. In particular, the Bidirectional Associative Memory (BAM) model has shown great promise for pattern recognition for its capacity to be trained using a supervised or unsupervised scheme. This paper describes such a BAM, one that can encode patterns of real and binary values, perform multistep pattern recognition of variable‐size time series and accomplish many‐to‐one associations. Moreover, it will be shown that the BAM can be generalized to multiple associative memories, and that it can be used to store associations from multiple sources as well. The various behaviors are the result of only topological rearrangements, and the same learning and transmission functions are kept constant throughout the models. Therefore, a consistent architecture is used for different tasks, thereby increasing its practical appeal and modeling importance. Simulations show the BAM′s various capacities, by using several types of encoding and recall situations.

A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties

In this paper, we present a new recurrent bidirectional model that encompasses correlational, competitive and topological model properties. The simultaneous use of many classes of network behaviors allows for the unsupervised learning/categorization of perceptual patterns (through input compression) and the concurrent encoding of proximities in a multidimensional space. All of these operations are achieved within a common learning operation, and using a single set of defining properties. It is shown that the model can learn categories by developing prototype representations strictly from exposition to specific exemplars. Moreover, because the model is recurrent, it can reconstruct perfect outputs from incomplete and noisy patterns. Empirical exploration of the model's properties and performance shows that its ability for adequate clustering stems from: (1) properly distributing connection weights, and (2) producing a weight space with a low dispersion level (or higher density). In addition, since the model uses a sparse representation (k-winners), the size of topological neighborhood can be fixed, and no longer requires a decrease through time as was the case with classic self-organizing feature maps. Since the model's learning and transmission parameters are independent from learning trials, the model can develop stable fixed points in a constrained topological architecture, while being flexible enough to learn novel patterns.

A Bidirectional Hetero-Associative Memory for True-Color Patterns

Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of true-color patterns.

A Bidirectional Heteroassociative Memory for Binary and Grey-Level Patterns

Typical bidirectional associative memories (BAM) use an offline, one-shot learning rule, have poor memory storage capacity, are sensitive to noise, and are subject to spurious steady states during recall. Recent work on BAM has improved network performance in relation to noisy recall and the number of spurious attractors, but at the cost of an increase in BAM complexity. In all cases, the networks can only recall bipolar stimuli and, thus, are of limited use for grey-level pattern recall. In this paper, we introduce a new bidirectional heteroassociative memory model that uses a simple self-convergent iterative learning rule and a new nonlinear output function. As a result, the model can learn online without being subject to overlearning. Our simulation results show that this new model causes fewer spurious attractors when compared to others popular BAM networks, for a comparable performance in terms of tolerance to noise and storage capacity. In addition, the novel output function enables it to learn and recall grey-level patterns in a bidirectional way.