• Journal of Information Processing Systems
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• v.20 no.2
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• pp.252-262
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• 2024

The multi-hop wireless sensor network (WSN) suffers from energy limitation and eavesdropping attacks. We propose a simple and energy-efficient convergecast mechanism using inter-flow random linear network coding that can provide confidentiality to the multi-hop WSN. Our scheme consists of two steps, constructing a logical tree of sensor nodes rooted at the sink node, with using the Bloom filter, and transmitting sensory data encoded by sensor nodes along the logical tree upward to the sink where the encoded data are decoded according to our proposed multi-hop network coding (MHNC) mechanism. We conducted simulations using OMNET++ CASTALIA-3.3 framework and validated that MHNC outperforms the conventional mechanism in terms of packet delivery ratio, data delivery time and energy efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.714-727
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• 2015

The hierarchical structure in networks is widely applied in many practical scenarios especially in some emergency cases. In this paper, we focus on a tree network with and without packet loss where one source sends data to n destinations, through m relay nodes employing random linear network coding (RLNC) over a Galois field in parallel transmission systems. We derive closed-form probability expressions of successful decoding at a destination node and at all destination nodes in this multicast scenario. For the convenience of computing, we also propose an upper bound for the failure probability. We then investigate the impact of the major parameters, i.e., the size of finite fields, the number of internal nodes, the number of sink nodes and the channel failure probability, on the decoding performance with simulation results. In addition, numerical results show that, under a fixed exact decoding probability, the required field size can be minimized. When failure decoding probabilities are given, the operation is simple and its complexity is low in a small finite field.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.124-131
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• 1994

In this paper, we propose a new approach of solving the stereopsis problem with a discrete-time cellular neural network(DTCNN) where each node has connections only with its local neithbors. Because the matching process of stereo correspondence depends on its geometrically local characteristics, the DTCNN is suitable for the stereo correspondence. Moreover, it can be easily implemented in VLSI. Therefore, we employed a two-layer DTCNN with dual templates, which are determined with the back propagation learning rule. Based on evaluation of the proposed approach for several random-dot stereograms, its performance is better than that of the Marr-Poggio algorithm.

• Journal of IKEEE
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• v.26 no.4
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• pp.614-621
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• 2022

This paper proposes RIDS (Random Forest-Based Intrusion Detection), which is an intrusion detection system to detect hacking attack based on random forest. RIDS detects three typical attacks i.e. DoS (Denial of service) attack, fuzzing attack, and spoofing attack. It detects hacking attack based on four parameters, i.e. time interval between data frames, its deviation, Hamming distance between payloads, and its diviation. RIDS was designed in memory-centric architecture and node information is stored in memories. It was designed in scalable architecture where DoS attack, fuzzing attack, and spoofing attack can be all detected by adjusting number and depth of trees. Simulation results show that RIDS has 0.9835 accuracy and 0.9545 F1 score and it can detect three attack types effectively.

• Journal of Gastric Cancer
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• v.22 no.2
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• pp.120-134
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• 2022

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions: Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5517-5523
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• 2012

Purpose: To evaluate the diagnostic accuracy of ultrasonograph and fine-needle aspiration cytologic examination (USG-FNAC) in the staging of axillary lymph node metastasis in breast cancer patients.Methods: We conducted an electronic search of the literature addressing the performance of USG-FNAC in diagnosis of axillary lymph node metastasis in databases such as Pubmed, Medline, Embase, Ovid and Cochrane library. We introduced a series of diagnostic test indices to evaluate the performance of USG-FNAC by the random effect model (REM), including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios and area under the curve (AUC). Results: A total of 20 studies including 1371 cases and 1289 controls were identified. The pooled sensitivity was determined to be 0.66 (95% CI 0.64-0.69), specificity 0.98 (95% CI 0.98-0.99), positive likelihood ratio 22.7 (95% CI 15.0-34.49), negative likelihood ratio 0.32 (95% CI 0.25-0.41), diagnostic OR 84.2 (95% CI 53.3-133.0). Due to the marginal threshold effect found in some indices of diagnostic validity, we used a summary SROC curve to aggregate data, and obtained a symmetrical curve with an AUC of 0.942. Conclusion: The results of this meta-analysis indicated that the USG-FNAC techniques have acceptable diagnostic validity indices and can be used for early staging of axillary lymph node in breast cancer patients.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1072-1081
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• 2008

We can categorize the network address allocation mechanism into two types. One is to assign a unique network address using the address allocation server and the other is to make a random address by itself and process the DAD(Duplicate Address Detection) procedure. In this paper we suggest a new address allocation mechanism based on the signal strength for the PLC-based home network. As the combined mechanism of two types this mechanism allocates a unique address for the new node from one of the existing nodes with the simple equation and with the priority based on the signal strength from the new node to the existing nodes. We can use this mechanism for the self-healing function when the packet from the source node may not be delivered to the destination node directly. We developed the simulator for our mechanism using the C# programming and verified the network address assigned uniquely based on the signal strength.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.107-115
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• 2009

The cryptological key which is used in WiMAX environment is used at regular intervals by mobile nodes (laptop computer, PDA, cell-phone) which is in the range of base station coverage. But it is very weak at local attack like man-in-the-middle when the mobile node is off the range of base station or enters into the range to communicate with base station because the communication section is activated wirelessly. This paper proposes a distribution key building protocol which can reuse security key used by nodes to reduce cryptological security attack danger and communication overhead which occurs when mobile node tries to communicate with base station. The proposed distribution key establishing protocol can reduce overhead which occurs between base station and mobile node through key reusing which occurs during the communication process and also, makes security better than IEEE 802.16 standard by creating shared key which is required for inter-certification through the random number which node itself creates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.227-247
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• 2018

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.