• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.59-62
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• 2000

As the density of memories increases, unwanted interference between cells and coupling noise between bit-lines are increased and testing high density memories for a high degree of fault coverage can require either a relatively large number of test vectors or a significant amount of additional test circuitry. From now on, conventional test algorithms have focused on faults between neighborhood cells, not neighborhood bit-lines. In this paper, a new algorithm for NPSFs, and neighborhood bit-line sensitive faults (NBLSFs) based on the NPSFs are proposed. Instead of the conventional five-cell and nine-cell physical neighborhood layouts to test memory cells, a three-cell layout which is minimum size for NBLSFs detection is used. To consider faults by maximum coupling noise by neighborhood bit-lines, we added refresh operation after write operation in the test procedure(i.e., write \longrightarrow refresh \longrightarrow read). Also, we present properties of the algorithm, such as its capability to detect stuck-at faults, transition faults, conventional pattern sensitive faults, and neighborhood bit-line sensitive faults.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.208-215
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• 2013

Early iterations of the existing Global Positioning System (GPS)-based or radio lateration technique-based vehicle localization algorithms suffer from flip ambiguities, forged relative location information and location information exchange overhead, which affect the subsequent iterations. This, in turn, results in an erroneous neighbor-vehicle map. This paper proposes an extended information overlap measure (EIOM) algorithm to reduce the flip error rates by exchanging the neighbor-vehicle presence features in binary information. This algorithm shifts and associates three pieces of information in the Moore neighborhood format: 1) feature information of the neighboring vehicles from a vision-based environment sensor system; 2) cardinal locations of the neighboring vehicles in its Moore neighborhood; and 3) identification information (MAC/IP addresses). Simulations were conducted for multi-lane highway scenarios to compare the proposed algorithm with the existing algorithm. The results showed that the flip error rates were reduced by up to 50%.

• The Journal of Information Systems
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• v.8 no.1
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• pp.27-44
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. CBR has several research issues which can be divided into two categories : (1) static issues and (2) dynamic issues. The static issues are related to case representation scheme and case data model, that is, focus on casebase which is a repository of cases. The dynamic issues, on the other hand, are related to case retrieval procedure and problem solving process, i.e. case adaptation phase. This research is forcused on retrieval procedure Traditional query processing accepts precisely specified queries and only provides exact answers, thus requiring users to fully understand the problem domain and the casebase schema, but returning limited or even null information if the exact answer is not available. To remedy such a restriction, extending the classical notion of query answering to approximate query answering(AQA) has been explored. AQA can be achieved by neighborhood query answering or associative query answering. In this paper, neighborhood query answering technique is used for AQA. To reinforce the CBR process, a new retrieval procedure(cooperative CBR) using neighborhood query answering is proposed. An neighborhood query answering relaxes a query scope to enlarge the search range, or relaxes an answer scope to include additional information. Computer Aided Process Planning(CAPP) is selected as cooperative CBR application domain for test. CAPP is an essential key for achieving CIM. It is the bridge between CAD and CAM and translates the design information into manufacturing instructions. As a result of the test, it is approved that the problem solving ability of cooperative CBR is improved by relaxation technique.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.428-432
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• 2007

We present a novel method for determining k nearest neighbors, which accurately recognizes the underlying clusters in a data set. To this end, we introduce the "tiling neighborhood" which is constructed by tiling a number of small local circles rather than a single circle, as existing neighborhood schemes do. Then we formulate the problem of determining the tiling neighborhood as a minimax optimization, leading to an efficient message passing algorithm. For several real data sets, our method outperformed the k-nearest neighbor method. The results suggest that our method can be an alternative to existing for general classification tasks, especially for data sets which have many missing values.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.963-971
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• 1990

This paper proposes a memory intensive VLSI architecture for the realization of real-time 3x3 neighborhood processor based on the distributed arithmetic. The proposed architecture is characterized by a bit serial and multi-kernel parallel processing which exploits the pixel kernel parallelism and concurrency. The chip implements 8 neighborhood processing elements in parallel with efficirnt input and output modules which operate concurrently. Besides the a4chitectural design of a neighborhood processor, the design methodology using module generator concept has been considered and MOGOT(MOdule Generator Oriented VLSI design Tool) has been constructed based on the workstation. Based on these design environments MOGOT, it has been shown that the main part of the suggested architecture can be designed efficiently using 2\ulcorner double metal CMOS technology. It includes design of input delay and data conversion module, look-up table for inner product operation, carry save accumulator, output data converter and delay module, and control module.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.737-745
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• 2008

In this paper, a novel neighborhood metric of histogram equalization (HE) algorithm for contrast enhancement is presented. We present a refinement of HE using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We tested a novel sorting key with the suggestion of using the original image greylevel as the primary key and a novel neighborhood distinction metric as the secondary key, and compared HE using proposed distinction metric and other HE methods such as global histogram equalization (GHE), HE using voting metric and HE using contrast difference metric. We found that our method can preserve advantages of other metrics, while reducing drawbacks of them and avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.119-135
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• 2012

Among various recommendation techniques, neighborhood-based Collaborative Filtering (CF) techniques have been one of the most widely used and best performing techniques in literature and industry. This paper proposes new approaches that can enhance the neighborhood-based CF techniques by identifying a few best neighbors (the most similar users to a target user) more accurately with more information about neighbors. The proposed approaches put more weights to the users who have more items co-rated by the target user in similarity computation, which can help to better understand the preferences of neighbors and eventually improve the recommendation quality. Experiments using movie rating data empirically demonstrate simultaneous improvements in both recommendation accuracy and diversity. In addition to the typical single rating setting, the proposed approaches can be applied to the multi-criteria rating setting where users can provide more information about their preferences, resulting in further improvements in recommendation quality. We finally introduce a single metric that measures the balance between accuracy and diversity and discuss potential avenues for future work.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.3
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• pp.118-125
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• 2004

The purpose of this study is to decide an appropriate neighborhood and a transition rule of cellular automata by analyzing the past growth process of urban areas in Gimhae. With cellular automata which can manage the change based on the dynamic model and time, this study analyzes the urban growth of Gimhae from 1987 to 2001. Also, through the simulation of different types for neighborhood and transition rules, we can find the appropriate neighborhood and the transition rule for Gimhae. In conclusion, the forecast of physical urban growth pattern is more accurate under conditions when the number of matrixes for the neighborhood is small, the shape of the neighborhood is rectangular, "${\alpha}$" value, which control the pace of urban growth, is low and the transition possibility ($P_{ij}$) is high.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.34-43
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• 2010

Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

• IE interfaces
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• v.15 no.4
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• pp.338-348
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• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.